Computing 2020

## Dedication

This work is dedicated to my lovely husband for his understanding of my
commitment to my research and for always encouraging me to be the best version
of myself, and to my mother, father, and little sister without whose
inspiration, drive and support I might not be the person I am today. It is
also dedicated to my supervisor, Dr Amin Beheshti, for his support and his
strong commitment to teaching. He believed in me, helped me become a better
researcher, and shed light on the issues I faced during my PhD studies. I also
thank my associate supervisor Professor Mehmet Orgun for always being
supportive and sharing his knowledge with me.

At the end, as Winston S. Churchill once said, ‘Success is not final; failure
is not fatal: It is the courage to continue that counts’. I hope this thesis
will be a starting point for my future research career. Last, but not the
least, I dedicate this work to Macquarie University and the community that
gave me the chance to conduct my research over the past three years.

## Acknowledgements

I would like to express my sincere appreciation to Dr Amin Beheshti, my
principal supervisor, for all his support, encouragement, and endless guidance
during my PhD study. Furthermore, I would like to express my deepest
appreciation to him for always helping me put myself in the right direction
with his attitude for a high quality of research. It has been my great fortune
to have Dr Beheshti as my supervisor at Macquarie University
(MQU111https://www.mq.edu.au/).

I would like to also thank my associate supervisor Prof Mehmet Orgun who has
always supported me in various stages of my research.

I am thankful to my team members in the Data Analytics Research
Lab222https://data-science-group.github.io/ at MQU for their friendship,
support and helpful comments. In addition, I would like to thank the review
panels and the anonymous reviewers who provided suggestions and helpful
feedback on my publications.

I would also like to thank the staff members of the Department of Computing at
Macquarie University for their administrative help.

Last, but not the least, I would like to dedicate this thesis to my lovely
family, for their love, patience, and understanding. They allowed me to spend
much of my time on this thesis. Their love, support, and encouragement have
been the foundation of my life. Without their endless love and inspiration,
this thesis would have never been started nor completed.

## Dissertation Examiners

  * •

Professor Abdul Sattar, Griffith University, Australia

  * •

Associate Professor Aamir Cheema, Monash University, Australia

  * •

Associate Professor Hakim Hacid, Zayed University, UAE

## Publications

This thesis is based on my research conducted during my PhD program at the
Department of Computing, Macquarie University, between 2017 and 2020. Some
parts of my research have been published at the following venues:

  * $\bullet$

Shahpar Yakhchi, Amin Beheshti, Seyed Mohssen Ghafari, Mehmet Orgun, and
Guanfeng Liu, ‘Towards a Deep Attention-based Sequential Recommender System’,
IEEE Access, vol. 8, pp. 178073–178084, 2020. (Impact Factor: 3.75)

  * $\bullet$

Shahpar Yakhchi, Amin Beheshti, Seyed Mohssen Ghafari, and Mehmet Orgun,
‘Enabling the Analysis of Personality Aspects in Recommender Systems’,
Published in the Proceedings of $26^{th}$ Pacific Asia Conference on
Information Systems (PACIS), Xian, China, pp. 1–15, 2019. (Core Rank: A)

  * $\bullet$

Shahpar Yakhchi, Seyed Mohssen Ghafari, Amin Beheshti, and Mehmet Orgun, ‘CNR:
Cross-network Recommendation Embedding User’s Personality’, Published in the
Proceedings of Web Information Systems Engineering (WISE) Workshop QUAT’18,
pp. 62–77, 2018. (Core Rank: A)

  * $\bullet$

Shahpar Yakhchi, Seyed Mohssen Ghafari, and Mehmet Orgun, ‘TAP: A Two-Level
Trust and Personality-Aware Recommender System’, Accepted by The 1st
International Workshop on AI-enabled Process Automation, ICSOC, December 2020.
(Core Rank: A)

  * $\bullet$

Amin Beheshti, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari,
Srinivasa Reddy Goluguri, and Mohammad Amin Edrisi, ’Towards Cognitive
Recommender Systems’, Algorithms Journal 2020, 13(8), pp. 1–27.

  * $\bullet$

Seyed Mohssen Ghafari, Shahpar Yakhchi, Amin Beheshti, and Mehmet Orgun,
‘Social Context-Aware Trust Prediction: Methods for Identifying Fake News’,
Published in the Proceedings of Web Information Systems Engineering (WISE),
pp. 161–177, 2018. (Core Rank: A)

  * $\bullet$

Seyed Mohssen Ghafari, Shahpar Yakhchi, Amin Beheshti, and Mehmet Orgun,
‘SETTRUST: Social Exchange Theory Based Context-Aware Trust Prediction in
Online Social Networks’, Published in Web Information Systems Engineering
(WISE) Workshop QUAT’18, pp. 46–61, 2018. (Core Rank: A)

  * $\bullet$

Amin Beheshti, Vahid Moraveji Hashemi, Shahpar Yakhchi, Hamid Reza Motahari-
Nezhad, Seyed Mohssen Ghafari, and Jian Yang, ‘Personality2vec: Enabling the
Analysis ofBehavioural Disorders in Social Networks’, Published in the
Proceedings of $13^{th}$ ACM International WSDM Conference, Houston, USA,
Texas, pp. 1–4, 2020. (Core Rank: A*)

  * $\bullet$

Amin Beheshti, Vahid Hashemi Moraveji, and Shahpar Yakhchi, ‘Towards Context-
Aware Social Behavioral Analytics’, Published by the 17th International
Conference on Advances in Mobile Computing & Multimedia (MoMM2019), Germany,
December, 2019. (Core Rank: B)

  * $\bullet$

Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cecile Paris, Shahpar
Yakhchi, Alireza Jolfaei, Mehmet Orgun, Quan Z. Sheng, and Jia Wu, ‘Modeling
Personality Effect in Trust Prediction’, Published by Journal of Data
Intelligence 2021.

  * $\bullet$

Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cecile Paris, Shahpar
Yakhchi, Alireza Jolfaei, and Mehmet Orgun, ‘A Dynamic Deep Trust Prediction
Approach for Online Social Networks’, Accepted by the $18^{th}$ International
Conference on Advances in Mobile Computing and Multimedia (MoMM’20). (Core
Rank: B)

  * $\bullet$

Seyed Mohssen Ghafari, Aditya Joshi, Amin Beheshti, Cecile Paris, Shahpar
Yakhchi, and Mehmet Orgun, ‘DCAT: A Deep Context-Aware Trust Prediction
Approach for Online Social Networks’, Published in the Proceedings of
$17^{th}$ International Conference on Advances in Mobile Computing and
Multimedia (MoMM’19), Munich, Germany, December, pp. 1–8, 2019. (Core Rank: B)

  * $\bullet$

Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cecile Paris, Shahpar
Yakhchi, and Mehmet Orgun, ‘Intelligent Trust Prediction: Methods for
Identifying Fake News’, Accepted by the Cyber Defence Next Generation
Technology and science Conference (CDNG), Brisbane, Australia, 2020.

  * $\bullet$

Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cecile Paris, Adnan
Mahmood, Shahpar Yakhchi, and Mehmet Orgun; ‘A Survey on Trust Prediction in
Online Social Networks’, IEEE Access, vol. 8, pp. 144292–144309, 2020. (Impact
Factor: 3.75)

## Abstract

Recommender systems (RSs) have emerged as very useful tools to help customers
with their decision-making process, find items of their interest, and
alleviate the information overload problem. There are two different lines of
approaches in RSs: (1) general recommenders with the main goal of discovering
long-term users’ preferences, and (2) sequential recommenders with the main
focus of capturing short-term users’ preferences in a session of user-item
interaction (here, a session refers to a record of purchasing multiple items
in one shopping event). While considering short-term users’ preferences may
satisfy their current needs and interests, long-term users’ preferences
provide users with the items that they may interact with, eventually.

In this thesis, we first focus on improving the performance of general RSs.
Most of the existing general RSs tend to exploit the users’ rating patterns on
common items to detect similar users. The data sparsity problem (i.e. the lack
of available information) is one of the major challenges for the current
general RSs, and they may fail to have any recommendations when there are no
common items of interest among users. We call this problem ‘data sparsity with
no feedback on common items’ (DSW-n-FCI). To overcome this problem, we propose
a personality-based RS in which similar users are identified based on the
similarity of their personality traits.

Next, we focus on one of the major difficulties that sequential recommenders
are confronted with, which is how to model a noisy session. Current studies
may assume that all the adjacent items in a session are highly dependent,
which may not be practical in real-world scenarios because of the uncertainty
of the customers’ shopping behaviour. A user-item interaction session may
contain some irrelevant items which in turn may lead to false dependencies.
Furthermore, long-term users’ preferences may be ignored by most of the
existing sequential recommenders. To address this issue, we propose an
attention-based framework to discriminately learn the dependencies among items
in both long-term and short-term users’ preferences.

Finally, sequential recommenders assume that each user-item interaction in a
session is independent, which may be a very simplistic assumption. In real-
world cases, people may have a particular purpose for buying successive items
in a session. Unfortunately, a user’s behaviour pattern is not completely
exploited by most of the current sequential recommenders, and they neglect the
distinction between users’ purposes and their preferences in both users’ long-
term and short-term item sets. We propose an approach using a purpose-specific
attention unit (PSAU) to attend to important items differently depending on
the user purposes and preferences for the next-item recommendation task. All
of the proposed models in this thesis were tested on real-world datasets, and
the experimental results demonstrated the effectiveness of these approaches as
compared to the state-of-the-art RSs.

###### Contents

  1. Dedication
  2. Acknowledgements
  3. Dissertation Examiners
  4. Publications
  5. Abstract
  6. 1 Introduction
    1. 1.1 Background
      1. 1.1.1 An Overview on Recommender Systems
      2. 1.1.2 Values and Significance of General Recommender Systems
      3. 1.1.3 Values and Significance of Sequential Recommender Systems
      4. 1.1.4 Values and Significance of Unified Recommender Systems
    2. 1.2 Research Motivations
      1. 1.2.1 Overview of Challenges and Issues
      2. 1.2.2 Summary of Research Progress and Gaps in RSs
    3. 1.3 Overview of Challenges of Recommender Systems
      1. 1.3.1 Dealing with Sparsity of User-Item Interactions
      2. 1.3.2 Handling a Noisy User-Item Sequence
      3. 1.3.3 Modelling Collective Sequential Dependencies
    4. 1.4 Research Contributions
    5. 1.5 Thesis Organisation
  7. 2 Related Work
    1. 2.1 Background
      1. 2.1.1 What is a Recommender System?
      2. 2.1.2 Why Recommender Systems?
      3. 2.1.3 How Does a Recommender System Work?
    2. 2.2 General Recommenders
      1. 2.2.1 Content-Based Recommendations
      2. 2.2.2 Collaborative Filtering-Based Recommendations
      3. 2.2.3 Hybrid-Based Recommendations
    3. 2.3 Sequential Recommenders
      1. 2.3.1 SRS Categorisation in terms of Session Characteristics and Challenges
      2. 2.3.2 SRS Categorisation in terms of Model Structure
      3. 2.3.3 SRSs Categorisation In terms of Inputs
    4. 2.4 Unified Recommenders
    5. 2.5 Summary
  8. 3 Fundamental Concepts and Preliminaries
    1. 3.1 Problem Definition for General Recommenders
    2. 3.2 Problem Definition for Sequential Recommenders
    3. 3.3 Preliminaries
      1. 3.3.1 Matrix Factorisation
      2. 3.3.2 Convolutional Neural Networks
      3. 3.3.3 Attention Mechanisms
  9. 4 Experimental Setup
    1. 4.1 Datasets
      1. 4.1.1 Amazon Instant Video Dataset
      2. 4.1.2 Tmall, Tafeng, and Gowalla Datasets
    2. 4.2 Evaluation Metrics
      1. 4.2.1 Accuracy Metrics
      2. 4.2.2 Novelty Metric
    3. 4.3 Baselines
      1. 4.3.1 Baselines for General Recommenders
      2. 4.3.2 Baselines for Sequential Recommenders
  10. 5 Enabling the Analysis of Personality Aspects in Recommender Systems
    1. 5.1 Introduction
      1. 5.1.1 The Target Problem and The Motivation
      2. 5.1.2 Proposed Design and Main Contributions
    2. 5.2 Overview and Framework
      1. 5.2.1 Problem Statement
      2. 5.2.2 Personality Acquisition
      3. 5.2.3  Acquiring Users’ Level of Knowledge
      4. 5.2.4 Our Framework
    3. 5.3 Experimental Evaluation of APAR and Analysis
      1. 5.3.1 Experimental Setup
      2. 5.3.2 Performance Comparison and Analysis
      3. 5.3.3 Impact of Personality Regularisation
      4. 5.3.4 DSW-n-FCI Experiments
    4. 5.4 Summary
  11. 6 Toward A Deep Attention-Based Sequential Recommender System
    1. 6.1 Introduction
      1. 6.1.1 The Target Problem and The Motivation
      2. 6.1.2 Proposed Design and Main Contributions
    2. 6.2 Overview and Framework
      1. 6.2.1 Problem Statement
      2. 6.2.2 Embedding Block
      3. 6.2.3 Attention Block
      4. 6.2.4 Fully Connected Block
      5. 6.2.5 Network Training
    3. 6.3 Experiment and Evaluation
      1. 6.3.1 Experimental Setup
      2. 6.3.2 Performance Evaluation
      3. 6.3.3 Evaluating User-Item Interaction Sequences with Noise
      4. 6.3.4 Impact of Different Types of Users’ Preferences
      5. 6.3.5 Impact of Hyper-Parameters
    4. 6.4 Summary
  12. 7 A Convolutional Attention Network for Unifying General and Sequential Recommenders
    1. 7.1 Introduction
    2. 7.2 The Target Problem and The Motivation
    3. 7.3 Proposed Design and Main Contributions
    4. 7.4 Overview and Framework
      1. 7.4.1 Problem Statement
      2. 7.4.2 Purpose Encoder
    5. 7.5 Experiment and Evaluation
      1. 7.5.1 Experimental Setup
      2. 7.5.2 Impact of Hyper-parameters
      3. 7.5.3 Impact of Different Session Lengths
      4. 7.5.4 Impact of Different Modules of CAN
      5. 7.5.5 Impact of PSAU Components
      6. 7.5.6 Overall Performance Comparison
    6. 7.6 Summary
  13. 8 Conclusions and Future Work
    1. 8.1 Conclusion
    2. 8.2 Future Work
      1. 8.2.1 General Recommender Systems’ Open Issues
      2. 8.2.2 Sequential Recommender Systems’ Open Issues

###### List of Figures

  1. 1.1 General summary of recommender systems’ categories.
  2. 1.2 Real example of a user’s shopping history; the blue frame shows a short-term user’s interest, while a long-term user’s preference is illustrated in a red frame.
  3. 1.3 Hierarchical structure of key issues and challenges of recommender systems. This structure has two main pillars, one with the main goal of taking the long-term preference (e.g. general recommenders), and the other reserved for emphasising integration short-term preference (e.g. sequential recommenders); there are two types of logical relations and views in this structure: $(1)$ a vertical view, dividing the problems of both the general and sequential recommenders into five levels separated by the dashed red arrows; $(2)$ a horizontal view which categorises all the sequential recommender challenges, on the right side and all the general recommender difficulties on the left side from the top to the bottom.
  4. 1.4 Structure of this thesis.
  5. 2.1 User shopping basket in real world.
  6. 2.2 General framework of a content-based recommender system, which offers another book similar to the one that Jimmy has purchased before.
  7. 2.3 General framework of a collaborative-based recommender system which recommends a set of CDs to Jimmy because of his similarity to Sarah.
  8. 2.4 Categorisation of collaborative filtering approaches from the technical perspective.
  9. 2.5 General framework of a sequential recommender system.
  10. 3.1 Overview of a rating matrix.
  11. 3.2 Matrix factorization structure.
  12. 3.3 Example for convolution operation on a 6*6 image with a 3*3 kernel.
  13. 3.4 Example of image portion for a max-pooling operation.
  14. 3.5 Fully connected layer.
  15. 4.1 Sample review of Amazon dataset.
  16. 4.2 Sample review of Gowalla dataset.
  17. 4.3 Overview of a confusion matrix.
  18. 4.4 ROC curve.
  19. 4.5 Overall framework of Hu [1], which consists of two main parts, namely rating-based and personality-based components.
  20. 4.6 Overall framework of TWIN [2] including data processor, personality recogniser, profile creator, similarity estimator, and results visualiser.
  21. 4.7 Graphical model for the CTR framework [3].
  22. 4.8 Left matrix represents the observed items. The right matrix stores positive feedback as 1, and the unobserved items as negative feedback, which is equal to 0 [4].
  23. 4.9 Left matrix records observed items by a user, while the right matrices are inferred by BPR which applies a pair-wise ranking [4].
  24. 4.10 Difference between Fossil and the current models, in which Fossil combines two different methods to provide a better suggestion [5].
  25. 4.11 Framework of HRM model: a two-layer hierarchical structure which builds a hybrid representation of the user and item interactions to predict the next purchased items [6].
  26. 4.12 Example of point-level, union-level, and skip-level behaviours in [7].
  27. 4.13 Architecture of SHAN in which a combination of long- and short-term users’ preferences are learned by a sequential hierarchical structure [8].
  28. 4.14 Very general design of the GRU4Rec network [9].
  29. 4.15 Encoder-decoder architecture of NARM model, where the user’s main purpose in the current session is captured by the local encoder, and the global encoder is used to model the user’s sequential behaviour; next, the combination of the results of both of these components is used to compute the recommendation score for each item [10].
  30. 4.16 Graphical structure of MEANS [11].
  31. 5.1 Framework of APAR, which consists of two major components: users’ personality detection and their level of knowledge extraction. In the personality detection module, ‘Psy. Cat’ refers to psychology categories which we extract from the users’ provided contents.
  32. 5.2 Impact of personality regularisation with respect to MAE evaluation metric on the Amazon dataset.
  33. 5.3 Impact of personality regularisation with respect to RMSE evaluation metric on the Amazon dataset.
  34. 5.4 Recommendation performance in the presence of DSW-n-FCI with respect to MAE evaluation metric on the Amazon dataset.
  35. 5.5 Recommendation performance in the presence of DSW-n-FCI with respect to RMSE evaluation metric on the Amazon dataset.
  36. 6.1 Graphical structure of DAS model, which consists of three main blocks: (i) Embedding Block to embed user and item into low-dimensional representations; (ii) Attention Block to emphasise on the importance of each item, $u^{(r,long)}$ and $u^{(r,short)}$ are the long-term and short-term users preferences, respectively, which are fed to the next block; and (iii) Fully Connected Block to first learn a mixture of users’ preferences and then predict the best next interacted items.
  37. 6.2 Performance comparison of the Gowalla and Tmall datasets in terms of AUC.
  38. 6.3 Performance comparison on the Gowalla and Tmall datasets in terms of Recall@N.
  39. 6.4 Effect of different session lengths.
  40. 6.5 Evaluation under novelty metric.
  41. 7.1 Architecture of CAN, which consists of two main modules, namely purpose encoder and preference encoder.
  42. 7.2 Impact of different embedding dimensions on Gowalla and Tmall datasets.
  43. 7.3 Impact of PSAU on Gowalla and Tmall datasets. W/O attention means no attention mechanism was used.

###### List of Tables

  1. 2.1 Comparison of different models of general recommenders.
  2. 2.2 Comparison of approaches used by sequential recommenders.
  3. 2.3 Summary of the influential factors of sequential recommender systems.
  4. 2.4 Comparison of approaches in recommender systems.
  5. 3.1 List of commonly used notations in this thesis.
  6. 4.1 Statistics of Tmall, Tafeng, and Gowalla datasets.
  7. 5.1 Examples of LIWC categories and corresponding words in each category.
  8. 5.2 Relationship between the Big Five personality traits and different LIWC categories [12] (in no particular order), based on the analysis from Pennebaker and King [13]. The Linguistic Inquiry and Word Count (LIWC) categorises words into more than 88 categories. Here, the categories of LIWC related to the Big Five personality traits, namely ‘Open.’, ‘Consc.’, ‘Extra.’, ‘Agree.’, and ‘Neuro.’, represent Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, respectively.
  9. 5.3 Comparison of the performance of APAR with other baseline approaches with respect to MAE and RMSE on the Amazon dataset.
  10. 6.1 Performance comparison on Gowalla and Tmall datasets in terms of Precision.
  11. 6.2 Performance evaluation on disordered Tmall dataset.
  12. 6.5 Impact of different types of users’ preferences on Recall@20.
  13. 6.8 Impact of different regularisation at Recall@20.
  14. 7.5.1 Experimental Setup
  15. 7.4 Impact of different session lengths.
  16. 7.7 Impact of CAN modules
  17. 7.8 Performance of different methods on Tmall dataset.
  18. 7.9 Performance of different methods on Gowalla dataset.

##  Chapter 1 Introduction

###  1.1 Background

####  1.1.1 An Overview on Recommender Systems

In the last two decades, we have witnessed the emerging growth of generated
information by people’s daily activities (e.g. browsing, clicking, listening
to music, and purchasing items). This may lead to an information overload
problem. Therefore, there is a need for a filtering tool which can deal with
this huge amount of data and overcome the information overload problem.
Recommender systems (RSs) have emerged as a filtering tool that can help
people with their daily living activities from eating and selecting clothes to
housing and travelling. Research on RSs has its roots in information retrieval
[14, 15], forecasting theories [16], user option modelling in business [17,
18], and cognitive science [19]. In the mid-1990s, RSs appeared as an
independent research domain [20]. In general, classic RSs try to understand
the users’ general tastes from the users’ and items’ historical interactions
(e.g. clicks, purchases, and likes). Therefore, RSs collect the users’
preferences on a set of items (e.g. movies, songs, books, applications,
websites, products, and services) explicitly (basically by analysing the
provided users’ ratings) or implicitly (typically by capturing the users’
activities, such as clicked on items, browsed websites, heard songs, and
watched movies). RSs are used to provide suggestions in environments such as
content-production industries and e-commerce platforms, e.g. Netflix
111https://www.netflix.com/, YouTube 222https://www.youtube.com/, and LinkedIn
333https://www.linkedin.com/, comparing member interests and creating
recommendation items corresponding to the categorised topics or other members.
For example, 80% of the movies watched on Netflix [21], 60% of the video
clicks on YouTube [22], and more than 50% of the hiring in LinkedIn can be
attributed to recommendations [23].

Figure 1.1: General summary of recommender systems’ categories.

An RS is known as one of the most valuable applications of machine learning
and artificial intelligence and has a considerable impact on every aspect of
our daily lives [24, 25]. To show the position of RSs in today’s cutting-edge
technologies, we present Figure 1.1, which is a summary of RSs’ division from
different aspects. Obviously, in this figure, if we contextually look into RSs
[26], there are a wide range of context-aware RSs, including personality-aware
[27], time-aware [28], location-aware [29], weather-aware [30], social-aware
[31], and trust-aware [32] RSs. In this figure, there are eight major
categories with different challenges and solutions. Each of these eight
classes of approaches has its distinct characteristics and is designed to
address a different set of problems.

For instance, if we take a domain (i.e. domain refers to the types of items
that share similar characteristics, such as movies or books [33]) as a context
(i.e. context is the information about the condition of an entity [34]),
traditional RSs can be roughly classified into two main categories: single-
domain recommenders, which recommend items belonging to a single domain, and
cross-domain recommenders, which aim to exploit knowledge from a source domain
to perform or improve recommendations in a target domain. The main focus of
this thesis is on the single-domain RSs and we leave the cross-domain
recommenders for a future work.

Basically, on the basis of how the users’ preferences are modelled, we can
classify approaches in a single domain RS into two main classes of general and
sequential recommenders. The main goal of general recommenders is to discover
the users’ long-term preferences by exploiting their past interactions with
items. Sequential recommenders try to understand the sequential users’
behaviours and capture short-term users’ preferences by modelling the item
dependency. We further discuss these two lines of recommenders and review the
existing studies in detail in Chapter 2.

General recommenders can be divided into three main categories according to
their input, goal, and mechanism: i) collaborative filtering models (CF): they
look at like-minded people to predict the users’ preferences, ii) content-
based approaches: use the past preferred items’ descriptions for the
recommendation process, and iii) hybrid methods: they combine some of the
previous methods in a unified manner. The core principle behind CF-based
techniques is that among all the users of online social networks (OSNs), those
who have had similar tastes in the past are more likely to share similar
interests in the future. The CF-based approaches can be grouped into two
different categories, namely memory-based and model-based approaches. Matrix
factorisation (MF) is one of the most popular methods in model-based CF
methods which learns users’ preferences by decomposing the original user-item
interaction matrix into the inner product of the two low-dimensional matrices.
MF as a dimensionality reduction technique factorises the user-item
interaction matrix in the latent features of the ratings [35, 36, 37].

Content-based approaches mostly rely on analysing item contents and user
profiles to recommend items that match with the users’ interests. One of the
most common methods in the content-based class of approaches is term
frequency/inverse document frequency (TF-IDF), which is a well-known measure
in information retrieval [14]. Each of the above content-based and CF
techniques has its own advantages and disadvantages. Hence, hybrid approaches
have appeared as a unified model which help to avoid certain limitations of
content-based and collaborative systems [38], [39].

In RSs, there are several successful real-world applications such as Netflix
which suggests movies and TV series and Last.fm 444https://www.last.fm/ that
offers songs and music albums. Usually, most of the aforementioned traditional
RSs treat a recommendation task as a matrix completion problem, in which a
sparse user-item rating matrix is given as an input. The main job of these RSs
is to predict the value of the missing rating, which by itself can demonstrate
the general users’ preferences (i.e. long-term users’ preferences). While
traditional approaches, which are generally well studied, are good at
modelling users’ general tastes, they usually ignore short-term users’
preferences, which may result in missing interest drifts of individual users
over time [40].

In contrast to the traditional RSs, sequential RSs (SRSs) are emerging as a
new line of RSs which differ from traditional RSs in terms of their input,
output, computational tasks, and abstract problem characterisation [41]. In
SRSs, a set of purchased items in one shopping transaction, which can be
considered a session, is given to the recommendation engine as an input.
Differently, SRSs take a sequence of user-item interactions to predict a set
of next user-item interactions, which is the SRSs’ output. In general, the
main purpose of SRSs is to model the sequential dependencies among user-item
interactions sequences and discover the hidden information embedded from one
sequence to another sequence. SRSs have attracted researchers’ attention to
different applications, such as point of interest (POI) recommendation [42],
[43], music recommendation [44], [45], and browsing recommendation [46].

Figure 1.2: Real example of a user’s shopping history; the blue frame shows a
short-term user’s interest, while a long-term user’s preference is illustrated
in a red frame.

Both types of the mentioned RSs have achieved considerable success and have
been applied in a large variety of areas, such as item recommendations, music
recommendations, movie recommendations, POI recommendations, news
recommendations, tag recommendations, friend recommendations, article
recommendations, and link recommendations. Moreover, there are more popular
real-world applications in different domains. For instance, Amazon is an
example of product recommendation [47], Facebook 555https://www.facebook.com/
and Twitter 666https://twitter.com/ are dominant social networking
applications, and YouTube is a good example of a multimedia sharing platform.

In general, a good RS should be able to build an accurate user’s profile by
tracing her/his activities and behaviours. If an RS can fully understand the
users’ needs and preferences, then it can provide satisfying suggestions,
accordingly. For instance, Figure 1.2 illustrates a typical online shopping
scenario. As is clear from a user’s historical interactions in the lower part
(red part) of this figure, the user may be a fan of ‘Harry Potter’, as she/he
has purchased or visited different ‘Harry Potter’-related products, such as
eyeglasses, magic wands, wine glasses, and books. On the basis of this
observation, we can infer that this user has been generally interested in
‘Harry Potter’-related items for a long time (i.e. user’s general interest).
This refers to the long-term user preferences (i.e. general interest), which
are mostly captured by a general recommender. In contrast, a user’s current
actions are shown in the upper part (blue part) of Figure 1.2. This user is
looking for a t-shirt according to her/his successive clicks history in
her/his previous transaction. This may refer to the short-term user
preferences (i.e. sequential behaviours), which are the most important concern
of a sequential recommender. While a general recommender may recommend another
‘Harry Potter’-related product, such as a necklace or a keychain, other
t-shirts or shorts will be recommended to the user by a sequential
recommender. Both types of user preferences have their own benefits; therefore
if we only consider the long-term user preferences, the users’ current
interests may not be taken into account. In contrast, considering only the
short-term user preferences may not be very interesting to the user as the
user’s general interest is ignored. Therefore, we can conclude that combining
the two types of user preferences can exploit different types of user
behaviours and making better suggestions for a user.

####  1.1.2 Values and Significance of General Recommender Systems

General recommenders have been widely studied in the last decade and the key
issue related to them is to discover users’ general tastes by capturing user-
item interactions, which can be acquired explicitly (e.g. ratings and
comments) or implicitly (e.g. clicks and purchasing history) [48], [4]. In
order to fully model users’ behaviours and preferences, there is an essential
need to trace each activity of the users. From Figure 1.2, we can infer that
most of the user’s activities in the long-term are related to ‘Harry Potter’.
First, she/he has purchased a ‘Harry Potter’-designed wine glass, then a pair
of eyeglasses of ‘Harry Potter’ was bought, and next a magic wand was added to
a wishlist; and finally, the user bought a set of ‘Harry Potter’ books. The
user’s historical transaction in the long-term can determine the user’s
general taste. As is shown in Figure 1.2, the user is generally interested in
‘Harry Potter’-associated products. Hence, this user is more willing to be
recommended another set of ‘Harry Potter’ collection, and thus, an RS which
can do this job, can satisfy the user’s general interest.

On the basis of the above observation, we understand that adopting a user’s
general interest can be a pillar of the user’s decision-making process. It is
an essential task for an RS to consider this important factor, as it may
influence the user’s decisions at present [49]. The reason behind the
importance of considering the user’s general taste is that it may be static
with no or slow change over time. For instance, take the above-mentioned
shopping basket as an example. The user is a fan of ‘Harry Potter’, and this
may not change for a long time. This means that the user may always be
interested in purchasing items related to ‘Harry Potter’.

To address the above issues, general RSs are proposed. The main concern of the
approaches in general recommenders is to fully exploit the user’s general
taste, which is usually stable. The basic unit of a general recommender is a
user-item interaction, which can be stored in a utility matrix, known as a
rating matrix. The matrix factorisation method and its variants are known as
dominant techniques for general recommenders in the existing literature [50],
[51]. Basically, approaches in general recommenders treat a recommendation
problem as a matrix completion task, as the original user-item interactions
matrix is very sparse. The missing values in this rating matrix need to be
predicted by a general recommender. This type of a RS has been well studied,
and various models have been proposed to deal with the mentioned issues.
However, as users’ behaviours are inherently variable, a general recommender
can be enriched by various additional factors such as context [52],
personality [53], [54], [55], [56], [57] and additional knowledge [58, 59, 60,
61, 62, 63, 64]. A comprehensive review of the existing techniques with
different assumptions and ideas which try to model the users’ general tastes
is given in Chapter 2.

####  1.1.3 Values and Significance of Sequential Recommender Systems

In contrast to the general recommenders, SRSs are another line of RSs with the
aim of modelling users’ sequential behaviour. Recently, sequential
recommenders have attracted considerable attention because of their
superiority in capturing short-term users’ preferences. Despite the general
RSs, with a basic unit of rating matrix, the basic unit of a sequential
recommender is a sequence of the user and item interaction logs (a session),
which are mostly available in practical applications [65], [66]. A session is
a timestamped list of past user actions, which can store multiple records of
purchased items in one shopping event [67]. Eventually, users’ clickstream
data which are recorded into a session are considered as an input of a
sequential recommender. In general recommenders, users’ IDs are available to
the system, where each row is dedicated to the one known user to save her/his
interactions with the items. In sequential recommenders, there is no need for
the users to be known by the system. Take the e-commerce domain as an example;
in this domain, users can freely perform actions while they are anonymous.
Users in sequential recommenders do not need to log into the system to place
different order types, such as viewing items, purchasing items, adding items
to cart, and adding items to wishlist. In addition, based on the application
domains, further detailed information can be extracted, such as the
descriptions of items during shopping for discounted products, or any other
extra user and item features. Finally, the user and item interaction sessions
and their attached available information are passed to the SRSs as the inputs,
and the ordered lists of items are recommended as the outputs.

Consider the real-world example in Figure 1.2. From this figure, we infer that
this user would like to buy a t-shirt with some design on it, as the user is
looking for many short-sleeve shirts and a set of t-shirts is added to her/his
personal cart. Hence, a sequential recommender may suggest a pair of shorts or
another type of shirt to the user, as many users search for a pair of shorts
after buying a t-shirt. Sequential recommenders capture the current needs and
interests of a user and then make a prediction accordingly to satisfy the
short-term users’ preference. To do so, there are varieties of models and
techniques which are widely adopted in the literature and can be roughly
categorised into two main classes: (a) model-free approaches which were
introduced between the late of 1990s and the early 2010s, and (b) model-based
methods which have been used from the early 2010s to the present [67]. The
model-free classes of approaches use data mining techniques, such as pattern
mining [68], association rule discovery [69] [70] [71] [72], and sequence
mining [73], to make a recommendation. While the model-free methods are
straightforward and easy to develop, the model-based approaches are
implemented to take advantage of the strength of machine learning techniques
in modelling complex data. Markov chain models [74] and recurrent neural
networks (RNNs) [75] are two dominant paradigms in SRSs’ domain. We further
discuss the existing works in the SRSs domain in detail in Chapter 2.

####  1.1.4 Values and Significance of Unified Recommender Systems

Both the aforementioned paradigms have strengths and weaknesses. While general
recommenders are good at modelling the long-term users’ preferences, the
current users’ interests are ignored in these approaches. Although the main
focus of SRSs is to model sequential dependencies by exploiting an item-to-
item co-occurrence matrix, the users’ general tastes are not taken into
consideration. Therefore, general recommenders, by considering the long-term
users’ preference, may better predict items that a user will be willing to
consume eventually, whereas sequential recommenders show their success in
recommending items that a user may interact with next, by modelling the short-
term users’ preferences.

Different from both the aforementioned frameworks, quite a few works have
attempted to take advantage of the ideas of both general and sequential
recommenders in one unified model. Note that in Figure 1.2, a blue dashed line
denotes the items which are recommended by a sequential recommender, whereas
those recommended by a general recommender are depicted by a red dashed line.
If we take a closer look at this figure, we can infer that ‘Harry Potter’
designed t-shirts which are surrounded by a green dashed line chart in the
middle part of this figure may better satisfy the users’ overall interests.
Unified recommenders with the idea of combining both types of users’
preferences have attracted more attention because of several reasons. First,
as illustrated in Figure 1.2, the user is browsing for a t-shirt and is
generally interested in ‘Harry Potter’-related products. Therefore, she/he
will be happy if a ‘Harry Potter’-designed t-shirt will be recommended to
her/him. Second, a unified recommender can create an opportunity for a user to
experience a novel item, which can increase the diversity rate of
recommendations. Third, understanding users’ behaviour and analysing their
preferences and needs make a recommender system smarter and more personalised.

Based on the above-mentioned observations, two major types of unified RSs are
proposed by the approaches in the literature. First, some approaches make the
key assumption of using one type of recommenders, either general or
sequential, to assist the other recommender. For example, HRM proposed by Wang
et al. [76] can achieve a better performance improvement of a sequential
recommender by incorporating the users’ general taste. In addition, the
adoption of different aggregation operations enables the HRM to model
complicated interactions. Second, certain approaches attempt to combine the
two general and sequential paradigms to benefit from the advantages of both
the models. Recurrent collaborative filtering (RCF) [77], for instance,
combines RNN and MF models within a single model to obtain a unified
recommender [78].

For the sequential part, the basic deep neural network models such as RNNs and
their variations such as Long Short-Term Memory unit (LSTM) [79], [80] and
Gate Recurrent Unit (GRU) [81] are mainly adopted because of their success in
capturing sequential dependencies. Different from RNNs, Convolutional Neural
Networks (CNNs) [82] and Graph Neural Networks (GNNs) [83] have also been
applied in the sequential part of a unified recommender in order to tackle the
possible problems of RNNs in dealing with long sequences. In addition to the
aforementioned models, a combination of some kind of basic deep neural
networks (e.g. RNN and CNN) [9] or some advanced models, such as attention
mechanisms [8], [84], memory networks [85], and mixture models [86], has been
proposed to build more powerful RSs. While it is important to consider both
types of users’ preferences, there are few limited works in which both types
of preferences have been modelled and more investigations are required to fill
this gap. We further discuss unified recommenders in Chapter 2. Next, we give
an introduction to the problems and the potential gaps which motivate our
proposed solutions in this thesis.

###  1.2 Research Motivations

In this section, we first present a brief overview of the key issues,
challenges, and complexities of recommender systems and then summarise the
possible solutions provided in the extant literature. Then, we introduce our
proposed ideas to deal with the shortcomings of the current studies.

Figure 1.3: Hierarchical structure of key issues and challenges of recommender
systems. This structure has two main pillars, one with the main goal of taking
the long-term preference (e.g. general recommenders), and the other reserved
for emphasising integration short-term preference (e.g. sequential
recommenders); there are two types of logical relations and views in this
structure: $(1)$ a vertical view, dividing the problems of both the general
and sequential recommenders into five levels separated by the dashed red
arrows; $(2)$ a horizontal view which categorises all the sequential
recommender challenges, on the right side and all the general recommender
difficulties on the left side from the top to the bottom.

####  1.2.1 Overview of Challenges and Issues

Despite decades of research, RSs still have many challenges, and more
investigations are required to overcome the difficulties that they may face.
In Figure 1.3, we summarise the main challenges in RSs. Given a hierarchical
structure consisting of five levels from the preference level to the attribute
level, we can see the main focus and goal of each level in the current RSs.
Based on the explanation presented thus far, we can roughly classify RSs on
the basis of the way they model user interest into two main classes of long-
term preference-based models and short-term preference-based approaches. While
long-term users’ preferences are modelled by a general RS, short-term users’
preferences are the main concern of a sequential RS. In general, there are two
types of logical relations and views of different levels of this complex
structure shown in Figure 1.3: (i) a horizontal view and (ii) a vertical view.
First, if we take a horizontal look at this complex structure, we can realise
that it contains two components which divide the existing challenges in RSs
into two main challenges: approaches with the main concern of considering
long-term users’ preferences (i.e. general recommenders), and methods with the
main focus of short-term users’ preferences (i.e. sequential recommenders).
Although each type of RS may have the same challenge as illustrated in Figure
1.3 (e.g. dealing with the items-related issues at the third level or handling
the attribute level at the fifth level), each type of the RSs need to solve
them differently in their own way as they have different inputs and
mechanisms. Second, if we vertically look at this design, we can find five
levels of problems from the preference level to the attribute level in both
the general and the sequential recommenders. At the item level, for instance,
while general recommenders deal with how to take an item’s content and its
heterogeneous features into account, sequential recommenders deal with
problems regarding the items’ dependency and their order in a session. Both
general and sequential RSs may be at the same vertical level, but their
challenges, inputs, and adopted mechanisms will be different, as they have
different natures and properties. Although the horizontal and vertical views
mostly cover the main issues in RSs, coupling between all of these levels and
views can be another logical relation in this design (e.g. at the preference
level, there might be approaches that aim to consider both long and short-term
users’ preferences at the same time). In particular, a comprehensive
explanation of the hierarchical structure is given in Chapter 2.

####  1.2.2 Summary of Research Progress and Gaps in RSs

In this section, we present a brief introduction to the current studies on
both general and sequential recommenders and the existing gaps in each class
of approaches.

##### General Recommender Systems

First, we start with a discussion of classic recommenders which focus mainly
on taking the long-term users’ preferences divided in collaborative filtering,
content-based, and hybrid approaches. In this thesis, we only focus on
collaborative filtering types of general recommenders. Collaborative
filtering-based models can be classified into two main classes: memory-based
and model-based [87], [88]. The main focus of memory-based approaches is to
calculate the similarity between a pair of either users or items to discover a
set of neighbourhoods for an active user and then make a prediction,
accordingly [89], [90]. The most widely adopted similarity metrics in this
regards are Pearson correlation coefficient [91] and cosine similarity [92].
However, the main problems which these approaches may face are the data
sparsity problem (i.e. when there is a lack of available information), the
cold-start problem (i.e. when a new user or item has just entered the system),
and the scalability problem (i.e. when the numbers of existing users and items
increase tremendously and computational resources go beyond the practical or
acceptable levels) [93].

While memory-based models are simple and easy to develop, their accuracy is
not sufficiently good specifically for online systems [94], and thus, model-
based algorithms motivated by statistical and machine learning methods have
emerged to recognise complex patterns from the rating matrix [95]. The well-
known model-based techniques are Bayesian-based models [87], [96], [97],
clustering models [98], [99], and latent semantic models [100]. Among all the
adopted techniques, MF is one of the most popular paradigms in the model-based
type of collaborative filtering, which is also widely utilised by the existing
works [101]. Although model-based approaches can show better performance than
the memory-based class of algorithms, the data sparsity problem still remains
the most important drawback of these approaches. Therefore, recommendation
engines start to use side information to enrich their available data and to
better overcome the data sparsity problem.

The increasing amount of generated data in each second motivates researchers
to use these new data as the auxiliary information, which goes beyond the
user-item matrix [102]. In general, there are different types of data which
can be used as side information, such as an item’s description, category, and
price, which can better present its properties [103], [104]. Moreover, users’
additional information such as age, gender, and their attitudes and behaviours
can be used by a recommender system to enrich the available information and
make a better suggestion, accordingly [105]. Incorporating user demographic
features into an RS cannot only result in performance improvement but also
better solve the cold-start problem [106]. Except for the above solutions for
the data sparsity problem, at the attribute level, a user’s personality type
as the user’s feature is incorporated into the RSs because of its considerable
impact on the users’ attitudes, tastes, and behaviours [107]. It is widely
recognised by psychologists that personality is a consistent behaviour pattern
that a person tends to show irrespective of her/his situation, and people with
similar personality types tend to share similar interests [108]. Personality
as a consistent behaviour pattern can be acquired either explicitly by filling
out a questionnaire or implicitly by observing the users’ behavioural
patterns. Among all the personality models, the most influential one is the
Five Factor Model (FFM) or the Big Five personality type model in both
psychology and computer science domains. FFM considers the five main
personality traits as Openness to Experience, Conscientiousness, Extraversion,
Agreeableness, and Neuroticism; it is typically abbreviated as OCEAN [109].
Therefore, inspired by the excellent capability of personality in revealing
human behaviour, some researchers have attempted to incorporate users’ big-
five personality traits into the process of generating recommendations [1],
[110]. Incorporating personality characteristics into RSs cannot only provide
users with a diverse set of items [111] but also provide a better group
recommendation [112], [27] and improve the accuracy of RSs for music, movies,
e-learning, and web searches [1], [113], [57]. For instance, Hu and Pu [1]
demonstrated the correlations between users’ personality types and their
interests in music genres, and then, they provided users with personality
quizzes to explicitly detect their personality traits; they used Pearson
correlation coefficient to understand how much these users are similar. Their
proposed model achieved a significant improvement over the CF approaches that
only take ratings into consideration for similarity detection. TWIN is another
example of a recommender system that estimates users’ similarity in terms of
their personality traits with the Euclidian distance [114], [2]. Furthermore,
Elahi et al. [115] developed a novel active learning strategy based on
personality in order to deal with the cold-start problem. In this setting,
users are asked to rate some items before they get recommendations. The
authors illustrated that incorporating users’ attributes such as age, gender,
and, particularly, their personality traits can have an important impact on
the users’ rating behaviour. They provided the Ten-Item Personality Inventory
(10-item TIPI) questionnaire in order to acquire the users’ personality types
explicitly, which may be relatively easy and accurate [116]. While effective,
obtaining users’ personality types is a challenging task, and more
investigations on the same are required.

##### Sequential Recommender Systems

In contrast to the general recommenders, which mainly consider the users’
long-term static preferences, SRSs have appeared as a new line of RSs which
mainly focus on short-term transactional patterns [67]. The main goal of a
sequential recommender is to model sequential dependencies, e.g. a customer
who has recently purchased an iPhone is more likely to buy an iWatch next.
SRSs can also refer to a session-based recommender system where the term of a
session can be considered a set of consumed or interacted components at one
time or within a certain period of time. For instance, a sequence of clicked
items in one hour or in one Internet surfing session can be stored in one
session. Recently, sequential recommenders have attracted more attention from
both the academia and the industry. There are several reasons behind this
fact. First, there is no need for the users to log in to the system, as they
may not prefer to share their IDs or other source of information because of
privacy concerns. Second, a sequential recommender can capture the current
users’ preferences and needs, which in turn results in personalised
suggestions. Third, the dynamic users’ taste is shown in a very recent
session, which can help to provide accurate recommendations. Fourth,
considering the short-term users’ preferences can provide novel offers for
users which they have not consumed already.

Different from general recommenders, SRSs identify the current interactions
between users and items to detect behavioural trends and interest shifts in
the user community. From a business point of view, sequential recommender
systems are more effective than the traditional recommender systems, as they
are applicable in many real-world cases, and they have access to a wide range
of data. However, a sequential recommender is relatively new and stands at its
early stage. In general, given the properties of transactional data, the
challenges of SRSs are two-fold from traditional models to the neural network-
based methods.

Sequential pattern mining (SPM) as one of the most popular techniques in the
area of data mining, is one of the traditional solutions for SRSs, in which
frequent patterns are discovered for further recommendations [117]. For
instance, Morales et al. [118] used SPM to discover new, interesting, and
useful knowledge from students’ usage information. Their model can
automatically provide links for new students while they are looking for a
course. While SPM is an intuitive answer for the next item recommendation
task, it may generate redundant patterns. Pattern/rule mining [119] as another
traditional paradigm tries to discover item co-occurrence for generating
recommendations [120], while simple the items’ context is ignored within a
session, which may mislead recommendations [121]. In contrast to most of the
traditional model-free models, the Markov Chain models are known as a
straightforward model-based technique for modelling item dependency in SRSs;
while the first-order MC models can capture simple sequential dependencies
[122], the higher-order ones can model complex relations [123]. MC can also be
combined with other models to better learn the users’ preferences. FPMC, as an
example, is a combination of MC and MF, in which instead of using the same
transition matrix for all the users, an individual transition matrix is used
for each user [74].

Despite traditional methods, neural network models have gained considerable
attention because of their success in modelling complex relations. RNNs, for
instance, as a powerful paradigm in deep learning, have shown promising
results in dealing with sequential data [124]. Although RNNs can exhibit
impressive performance, they may not be applicable to unordered session data
(e.g. when it does not matter which item is put in the cart first) because of
their strong assumption that there is a rigid order between adjacent items
[84]. Therefore, it is an important task to take a session’s characteristics
such as context, order, purpose, and length into consideration. As these
characteristics may differ from one session to another, approaches at the
interaction level try to handle these differences. Considering the session’s
context as an example, there may be some items in a session which are
irrelevant to the session’s context and may generate false dependencies.
Therefore, in order to avoid the impact of noisy items in a session, SRSs need
to pay more attention to contextual items. Some complex neural network
techniques such as attention networks [84] have been proposed to emphasise
more on the relevant items and reduce the impact of the irrelevant ones. For
instance, SHAN, a two-layer hierarchical network proposed by Ying et al. [8],
uses attention networks to automatically assign different weights to different
items in order to capture a session’s context.

The fourth level of the hierarchical diagram in Figure 1.3 is associated with
taking different types of actions into account. This is another important
issue of SRSs that has been less explored. Usually, most of the current
approaches treat users’ actions in the same way with no difference among them,
while there may be a difference between different action types such as
purchasing and adding to cart. This may limit the recommendation performance,
and it is important to take multi-behavioural sequences into consideration
[125], [126]. Most of the current SRSs assume that there is only one action in
each time step such as purchased items and they may ignore to take the other
types of actions such as viewing and clicking items. The involvement of items’
attributes, which is the focus of the attribute level, can help SRSs to better
understand the item dependency and better deal with the cold-start problem,
accordingly [127]. Moreover, it may be possible that the context of the items
affect the users’ next behaviours. Here, the context refers to the specific
explicit properties of an item when it is purchased, such as its popularity,
discount, and community trends, or the implicit ones, such as the time and
weather, season, and location, when the item was purchased [128], [129].

###  1.3 Overview of Challenges of Recommender Systems

In this thesis, we focus on the following three challenges in recommender
systems: dealing with the data sparsity problem, handling a noisy user-item
interaction sequence, and modelling a collective dependency among items in a
session. Next, we discuss these issues one by one in the following three
subsections.

####  1.3.1 Dealing with Sparsity of User-Item Interactions

General recommenders have been widely studied in the last decade, and the
collaborative filtering technique is known as a typical solution in this
regard. While the number of both users and items is very large in many large-
scale applications, there are only a few available rated items among the total
number of users. In particular, RSs may suffer from the data sparsity problem
under up to a 99% sparsity rate [130]. Lacking sufficient information
regarding users and items may have a significant impact on the performance of
a recommender system. Therefore, we explore a subclass of the data sparsity
problem called the Data Sparsity With no Feedback on Common Items (DSW-n-FCI)
problem, which refers to a situation when there is not much available
information about user-item interactions. In this situation, the current CF
models may fail as they mostly rely on rating patterns to find similar users
and cannot find any connections among users under the data sparsity problem.
To this end, we propose a personality-aware recommender system to discover
similar users even under the DSW-n-FCI condition. We demonstrated that there
were three main factors with a significant impact on the users’ decision-
making process in general recommenders, such as users’ personality traits,
users’ personal interests, and their level of knowledge. In Chapter 5, we will
discuss this framework and evaluate and compare it with state-of-the-art
methods in recommender systems to demonstrate its effectiveness.

####  1.3.2 Handling a Noisy User-Item Sequence

Recently, SRSs have attracted considerable attention from researchers. While
general recommenders are good at modelling a user’s general preferences, SRSs
try to model sequential dependencies among items. While considering a user’s
sequential behaviour (i.e. short-term preferences), which is reflected by the
recently observed items, is good at discovering a user’s current interest, a
user’s general taste which plays an important role in modelling her/his user’s
preferences is ignored by most of the SRSs. Therefore, better performance is
achieved by approaches that combine both long and short-term users’
preferences. Although the benefit from unifying these types of users’
preferences is common and important and has shown potential in SRSs, it has
not been sufficiently studied yet. For instance, Factorising Personalised
Markov Chain (FPMC), as one of the pioneering model in this setting,
integrates MC as a commonly used method in SRSs to model sequential behaviours
and MF for modelling the users’ long-term preferences. FPMC just utilises a
linear aggregation function to combine these two different types of users’
preferences [74]. More importantly, most of the existing studies assume that
there is a rigid order among items in a sequence, which may be a very
simplistic assumption. There may be noisy items in a shopping basket, which
may lead us to false dependency modelling in SRSs. In order to overcome the
above-mentioned problems, in Chapter 6, we particularly focus on how to
jointly model both long and short-term users’ preferences and how to learn the
different contributions of each item to both types of users’ interests for the
next item recommendation task.

####  1.3.3 Modelling Collective Sequential Dependencies

Although unifying both general and sequential users’ behaviour has shown a
great result, the reported studies on SRSs have not paid considerable
attention to this issue yet. Most of the current approaches either do no take
both types of users’ preferences or just linearly combine them together.
Moreover, one of the drawbacks of the current unified approaches is that they
assume that each user-item interaction in a session is independent and that
there is no particular purpose in buying successive items. However, there may
be a specific purpose behind purchasing a collective set of items. A common
example is that of a user who buys butter, milk, and flour together for making
a cake. In addition, different users may have different purposes and
preferences, and the same user may have various intentions. Thus, different
users may click on the same items with attention to a different purpose.
Therefore, a user’s behaviour pattern is not completely exploited by most of
the current methods, and they ignore to take the distinction between the
users’ purposes and their preferences in both the users’ long-term and short-
term item sets. Thus, in order to tackle the mentioned problems in Chapter 7,
we will describe how to discover a user’s purpose and her/his preferences over
a set of items and the level of each item’s contributions to both users’
purposes and preferences.

###  1.4 Research Contributions

The contributions of this thesis come from multiple folds and are summarised
below:

  * •

Proposing an MF Model Incorporating Users’ Personality, Users’ Personal
Interests, and Their Level of Knowledge

The existing general recommender systems that use collaborative filtering
highly depend on exploiting the users’ feedback, e.g. ratings and reviews on
common items to detect similar users. Thus, they might fail when there are no
common items of interest among users; this problem is called Data Sparsity
With no Feedback on Common Items (DSW-n-FCI). The current general recommenders
may fail to provide any suggestions when there are no common items of interest
among users. In contrast, personality-based RSs have emerged as an interesting
paradigm in general recommenders without any need for looking for common items
of interest to find similar users. Personality-based methods tend to discover
similar users in terms of their personality traits. A personality trait is a
consistent behaviour pattern that a person tends to show irrespective of
her/his situation, and has a strong correlation with individuals’ interests.
In addition, people with similar personality traits are more likely to consume
similar products. The above observations motivate us to take the users’
personality traits into account to make a better recommendation even under the
DSW-n-FCI problem. In contrast to most of the personality-based approaches
which detect the users’ personality traits explicitly through a questionnaire,
we ascertained the users’ personality traits implicitly with no burden on the
users (for more details, please refer to Chapter 5).

  * •

Proposing a Deep Attention-Based Sequential Recommender System

Most of the existing methods in SRSs assume that all the adjacent items in a
sequence are highly dependent, which may not be practical in real-world
scenarios because of the uncertainty of the customers’ shopping behaviour.
Thus, they may not be able to truly model the items’ dependencies. Moreover,
while there can be a significant improvement in recommendation performance by
capturing the long-term users’ preferences [131], it is mostly ignored by the
current SRSs. To overcome the mentioned problems, we propose a novel Deep
Attention-based Sequential (DAS) model to differently attend to item
contributions and discriminatively learn the dependencies among items in both
users’ long-term and short-term item sets. We use an attention mechanism to
emphasise the strongly context-relevant items and downplay the weakly
correlated ones in a user-item interaction sequence. Furthermore, we apply a
deep neural network to learn a combination of both long and short-term users’
preferences (for more details, please see Chapter 6).

  * •

Proposing a Convolutional Attention Network for Unifying General and
Sequential Recommenders Usually, people start shopping with a specific purpose
and keep looking into different items until a preferred one is found.
Moreover, different users may have different purposes for purchasing a set of
items. For instance, suppose that Alice and Rose buy a new bag. While Alice is
attracted to this bag because of its style and design which matches her shoes,
Rose wants to buy it as a gift. Moreover, the same user may have different
purposes and preferences by having the same actions on a set of items.
Therefore, we propose to use convolutional neural network (CNN) in order to
exploit the local context among a set of items in a long-term interacted item
set. We apply the CNN network in the users’ general preferences in order to
discover their main purposes. Next, we propose to use Purpose-Specific
Attention Unit (PSAU) in order to discriminately learn the representations of
the purpose and preference of the users. PSAU is used in the case of both long
and short-term users’ preferences to differently attend to different items and
fully exploit the different informativeness of different items (for more
details, please see Chapter 7).

###  1.5 Thesis Organisation

The remainder of this thesis is structured as follows:

Chapter 2 provides a comprehensive review of the recommendation system
approaches in two categories of classic recommenders and sequential
recommenders. First, we will give an introduction to demonstrate the
difference between these two categories of recommenders, followed by a further
categorisation of each type of recommenders. Next, we will review all the
categories one by one, from general recommenders to sequential recommenders.

Chapter 3 first formalises the recommendation problem with a set of required
definitions and notations. Then, we will introduce some preliminaries used in
this thesis to clarify them for the readers. In the end, we will present the
commonly used datasets, evaluation metrics, and baseline methods used in this
thesis.

Chapter 5 emphasises the significance of users’ behaviour such as personality
types for finding a neighbourhood in collaborative filtering-based methods. We
will present the details of our novel matrix factorisation model, which
considers three critical factors that generally affect a user’s decision-
making process. We will then discuss the importance of the users’ personality
types in finding similar users, followed by the users’ personal interests and
their level of knowledge, as the key factors for increasing the
recommendations’acceptance rate. The empirical studies demonstrated the
effectiveness of integrating these three main elements in general recommenders
as compared to using the purely collaborative filtering-based models.

Chapter 6 identifies the noisy user-item interaction sequences in sequential
recommenders. The significance of taking the items’ contributions into account
is illustrated, and a framework to solve this issue is proposed. A
corresponding algorithm based on the attention mechanism is designed to
implement it. The experimental results over real-world transaction datasets
demonstrated the advantages of this work as compared to the state-of-the-art
models in this area of study.

Chapter 7 focuses on modelling a collective dependency among items in a
sequence in order to find a user’s main purpose by clicking on a set of items.
A framework together with a corresponding algorithm is presented to show how
to learn both the users’ preferences and purposes simultaneously for the
following session recommendation tasks. An empirical analysis of real-world
datasets is provided to evaluate the algorithm’s merits.

Chapter 8 concludes this thesis by summarising its contributions. We will also
discuss some possible future directions related to this thesis. A hierarchy of
the thesis is given below in Figure 1.4 to better present the layout of this
thesis.

Figure 1.4: Structure of this thesis.

##  Chapter 2 Related Work

In this chapter, we discuss the current general and sequential RSs and provide
an extensive overview on state-of-the-art approaches.

Recommender Systems (RSs) are decision-making tools and techniques that
provide suggestions for items in which a particular user may be interested
[132]. Moreover, in the age of Big Data, many companies have started to trace
the online activities of customers and capture every click of the mouse in the
searching, browsing, comparing, and purchasing process. Given this huge amount
of data and meta-data generated every second, RSs have an opportunity to make
effective use of the captured data and provide a useful alternative to search
algorithms. As shown in Figure 2.1, Jimmy starts shopping at $t-k$; he first
purchases a book from the computer science domain. Then, after a while, he
makes a successive action at time $t$, by first putting a coffee machine onto
his wishlist, then buying a set of double-walled glasses and cinnamon powder,
and lastly clicking on an electrical kettle. Here, a recommender system should
trace his activities and behaviours to determine the best set of items that
meet his interests and needs. Therefore, recommender systems have appeared as
a kind of a platform which automatically recommends a small set of items in
order to help users find their desired items in online services.

In this chapter, a comprehensive overview of the existing RS approaches is
given. In particular, we discuss the attention of the community in two
different lines of research, namely general recommenders and sequential
recommenders.

Figure 2.1: User shopping basket in real world.

###  2.1 Background

####  2.1.1 What is a Recommender System?

The development of the Internet has resulted in the emerging growth of the
generated information including, but not limited to, texts, images, and
audios/videos. With this popularity, the number of users across the world is
increasing at a rapid speed because of the various social communication
networks such as Facebook 111https://www.facebook.com/ and Twitter
222https://twitter.com/, e-commerce products websites such as Amazon
333https://www.amazon.com/, and multimedia sharing platforms such as YouTube
444https://www.youtube.com/. As a large number of online users have easy and
instant access to the Internet, they can share and upload more information,
which leads to the information overload problem. These data can be acquired
explicitly by collecting a user’s ratings or implicitly by capturing a user’s
behaviours, such as clicked items, downloaded applications, and watched
movies. As stated by IBM, ‘Every day, Internet users generate 2.5 quintillion
bytes of information so much that 90% of the information on the planet today
has been generated in the most recent 2 years alone’
555https://www.ibm.com/products/software.

Therefore, there is an urgent need for a system to be able to filter the
generated information and to help people find their items of interest. To deal
with this issue, RS as an information retrieval system, has emerged to ease
users with their decision-making process, particularly in the era of big data
in which customers have access to more than countless products, services,
movies, websites, and other options. In this context, personalisation becomes
an essential factor which needs to be accounted for in RSs in order to assist
users for narrowing down their options, designing a better shopping
experience, and boosting business benefits. In general, a recommendation
engine generates a list of unobserved items of interest for a user on the
basis of the item’s characteristics, user’s descriptions, user’s past
behaviours and interactions with items, and some other sources of data such as
spatial, temporal, social, and individual information.

####  2.1.2 Why Recommender Systems?

Recommendation Systems (RSs) are used to provide suggestions in environments
such as content-production industries and e-commerce platforms, e.g. Netflix,
YouTube, and LinkedIn, comparing member interests and creating recommendation
items corresponding to the categorised topics or the other members. For
example, 80% of the movies watched on Netflix [21], 60% of the video clicks on
YouTube [133], and more than 50% of the hiring in LinkedIn come from
recommendations [23]. A key component of every e-commerce service is its
recommendation engine, which can help its customers with their options.
Understanding users’ preferences and their behaviours cannot only have a
significant effect on making a right prediction for them and make
recommendations more personalised but also improve the users’ satisfaction
rate and boost business profits and users’ loyalty.

####  2.1.3 How Does a Recommender System Work?

In general, on the basis of the modelled users’ preferences, there are two
types of recommender systems: general recommenders and sequential
recommenders. While the main goal of general recommenders is to discover the
users’ long-term preferences by exploiting their past items’ interactions,
sequential recommenders try to understand the sequential user behaviours and
model the short-term users’ preferences by modelling the dependency between
items. We will further discuss these two lines of recommenders and review the
existing studies in detail.

###  2.2 General Recommenders

There are different categories of recommender systems in the literature
according to multiple criteria such as the used techniques or based on a
specific domain. However, the most common division is based on the type of
data they use. Therefore, RSs can be broadly classified into two major
categories: content-based and collaborative filtering. We comprehensively
review each family of recommendation systems. At the end of this section, a
more detailed comparison between the major classes of approaches in general
recommender systems is provided in Table 2.1.

Figure 2.2: General framework of a content-based recommender system, which
offers another book similar to the one that Jimmy has purchased before.

####  2.2.1 Content-Based Recommendations

A general framework of a content-based recommender system is presented in
Figure 2.2. From this figure, we can observe that Jimmy is looking for a
computer science-related book, and finally, he bought one at time $t-k$. Next,
a content-based RS tries to find another book which is similar to the one that
he purchased before in terms of its contents, and then, another similar book
in the domain of computer science is recommended to him. The content-based
category of RSs has its roots in information retrieval [134]. In this
category, an RS learns to recommend items, those with similar contents to what
a user has liked in the past. As the name implies, content-based RSs mostly
rely on analysing the items’ contents and users’ profiles in order to
recommend items that are matched with the users’ interests [135]. Contents can
be a diverse range of information such as texts, images, and videos.

The process of content-based approaches is to first analyse the contents of an
item that a user has been interested in previously. Then, a user’s profile is
built on the basis of the extracted features from the previously interested
items. This is an important step in a family of content-based approaches, and
the better the profile construction is, the better is the users’ preferences
reflection and the more accurate is the recommendation. As the final step, a
recommendation engine analyses the similarity between a user’s profile and a
set of recommended items to the user. In order to build a user’s profile, the
user’s feedback needs to be collected and analysed. Users’ feedback can be
collected either explicitly, such as likes/dislikes, ratings, and writing
comments, or implicitly by saving, discarding, printing, and bookmarking the
interested items [19]. Most of the current content-based recommender systems
rely on exploiting textual content for constructing a user profile. Term
Frequency/Inverse Document Frequency (TF-IDF) weighting, as a well-known
keyword matching technique in information retrieval, is one of the most common
methods in this class which has been widely adopted by the approaches in
content-based RSs [14].

There are several attempts in the literature in this direction by adopting one
of the most popular techniques called, Word2Vec (W2V) to learn a low-level
representation of the item descriptions [136]. ITR [137], [138] is an example
of content-based recommendation system in which recommendations can be made in
a wide range of domains such as movies, books and music. Re:Agent, proposed by
Boone et al. [139] is an intelligent email agent that uses examples and
keywords provided by the user in order to extract features from documents.
Then, Re:Agent can take suitable actions such as filtering, downloading to
palmtops, and forwarding the email to voicemail, by using automatic feature
extraction. There are other attempts in a different range of domains such as
music [140], movies [141], and scientific search [142]. FOAF [143], [144] for
instance, is a music recommender system which builds a user profile based on
the user’s listening habits. Then, it can collect the information regarding
the listened songs such as an artist’s name, the song title, and a timestamp.
In addition, FOAF can understand the user’s psychological factors such as
personality, demographic preferences, and social relationships. Letizia as a
web assistance engine, is another example of a content-based recommender
system which discovers a user’s preference by collecting the user’s implicit
feedback such as bookmarking a page. Next, Letizia [145] tries to find a
keyword related to the users’ preferences.

Recently, deep learning has attracted significant attention because of its
capability to deal with the problems of traditional models and achieve a high
recommendation quality. For instance, in one study [146], a Convolutional
Neural Network (CNN) was used to better learn the characteristics of a song
that may affect the users’ preferences. The authors have tried to extract some
high-level properties such as genre, mood, instrumentation, and lyrical themes
from audio signals in order to provide an accurate music recommendation. Ask
Me Any Rating (AMAR) is another work that uses a Long Short-Term Memory (LSTM)
network to model textual content [147]. AMAR embeds the users’ preferences and
item characteristics in low-dimensional representations, and then, a list of
top-N items is recommended to a user according to the level of relevance,
which is calculated by a logistic regression layer.

Figure 2.3: General framework of a collaborative-based recommender system
which recommends a set of CDs to Jimmy because of his similarity to Sarah.

####  2.2.2 Collaborative Filtering-Based Recommendations

The pure CF recommender systems have better performance than the content-based
approaches, as CF-based systems can make a recommendation irrespective of the
content of an item, while content-based approaches highly depend on the
descriptions of users and items for making a prediction [148]. The core
principle behind CF-based techniques is that among all the users in an online
social network, those who had similar tastes in the past are more likely to
share similar interests in the future. Figure 2.3 presents a framework of a CF
model, in which Jimmy and Sarah made a similar action at time $t-k$, by
purchasing the same book, and thus, they may share similar interests in the
future. Based on this observation, a collaborative filtering recommender
system finds Jimmy and Sarah as similar users, and thus, it may suggest items
that are liked by Sarah to Jimmy, accordingly.

While there may be various types of CF-based categorisations in the
literature, we present a new division of approaches in this class of models in
Figure 2.4. In general, there are three main classes of techniques in CF-based
RSs, namely $(a)$ traditional CF, $(b)$ data enrichment, and $(c)$ neural
network techniques. ‘Tapestry’, as the email filtering system, was the first
CF-based recommender system, which was proposed by Goldberg et al. [149], to
coin the term ‘collaborative filtering (CF)’ in 1992. Tapestry is an
electronic message system that asks users for their feedback and then makes
suggestions for a target user by collecting feedback from the other users
[150], [151]. From the presented structure in Figure 2.4, we can see that
Matrix Factorisation (MF) is a traditional solution for collaborative
filtering models. MF is one of the most popular dimensionality reduction
techniques in model-based CFs which factorises a user-item interaction matrix
in latent features of the rating [35], [51]. User-item (U-I) interactions are
stored in the U-I matrix, which is used as an input to an appropriate
prediction model in order to generate recommendations. MF-based methods use
two different types of data: explicit feedback and implicit feedback. Models
that focus on the explicit feedback (e.g. rating and review) deal with the
rating prediction problem where users explicitly express their preferences by
providing a rating to particular items [51]. While approaches based on the
implicit feedback, (e.g. clicking and purchasing history) aim to formulate
recommendations as a ranking problem, this highly depends on the selection of
the objective loss function to optimise [152].

Figure 2.4: Categorisation of collaborative filtering approaches from the
technical perspective.

Next, we introduce similarity-based collaborative filtering methods, which try
to measure similarity among a set of items or users. If a similarity is
calculated between users, it can be known as a neighbourhood-based
collaborative filtering method, which looks for the best neighbourhood that
may share similar interests [153]. The term neighbours is used here to
represent the users in OSNs who have a pattern of rating items which is
similar to that of an active user. These approaches first capture the users’
preferences in order to construct her/his profile by collecting their given
ratings; a set of neighbours who have in common interested items is discovered
next. In the final step, when neighbours are identified, a recommender engine
provides a list of top-n interested items to the target user by analysing the
items in which neighbours are interested the most [154]. User-based CF
approaches predict users’ ratings on an unseen item by using similarity
metrics such as the Pearson correlation or the cosine similarity [155]. While,
items-based collaborative filtering approaches calculate the similarity
between items that a user has liked before for a recommendation task. The list
of recommended items, can be easily updated as they only need to retrieve
items that are similar to the previous ones with no need to re-train the model
again. Thus, personalisation may be easily achieved [156]. Although there is
an extensive research study in the literature where statistical measures such
as Pearson correlation and cosine similarity are used to calculate the
similarity in item-based CFs, these models may find it difficult to adapt to a
new dataset [157]. Recently, the sparse linear method (SLIM) [158] and
factored item similarity model (FISM) [159] have taken one step forward in
this direction by using data-driven methods to learn items similarity. The
former, Sparse Linear Method (SLIM), measures the prediction score for a new
item on the basis of the aggregation of other items in order to have a better
top-N recommendation task. While the latter, Factored Item Similarity Model
(FISM), calculates the product of two low-dimensional latent factor matrices
in order to learn the similarity between items. A few years later, a Global
and Local SLIM (GLSLIM) [160] was proposed as an extension of SLIM to overcome
the difficulties of SLIM in capturing users’ preferences. GLSLIM improves the
top-N recommendations task by providing a separate local item-item model for
every user subset.

According to Ning and Karypis [161], learning to rank (L2R) is a ‘popular
research area, as it directly models partial ordering relations between items,
which happens to be consistent with the top-N recommendation tasks. One key
element of L2R methods is the objective measures, defined as either ranking
error functions or optimisation metric’. For a better understanding: there are
two different types of actions in RSs, namely explicit actions and implicit
actions, whose results can create different recommendations’ inputs,
accordingly. The explicit actions refer to a situation in which users
explicitly present their preference on items by giving a rating, writing a
comment, and liking. Each of the mentioned explicit actions provides different
degrees of expressivity of the user’s preferences, but most of the existing
RSs use explicit types of inputs as they are more convenient to collect and
model [162]. In contrast to the explicit type of inputs, an implicit type of
inputs including purchase history, browsing history, search patterns, or even
mouse clicks should be modelled differently, as their nature is different from
that of the explicit ones. Modelling the implicit type of inputs may be more
complicated than modelling explicit ones, as there is no negative feedback
here. A user’s clicks on the items may show her/his interests in them, but it
may be difficult to understand a set of items that she/he may not be
interested in. For instance, if a user has purchased a particular item, it can
mean that she/he really likes this item, but it may be bought as a souvenir.
Hence, the implicit type of inputs is very noisy. Furthermore, users can give
a rating to the items of their interest, which is a numerical value from 1 to
5. In this setting, RSs can infer these values as the level of users’
interest, where a higher value implies more pleasant items. For example, items
which have received 5 star ratings can be considered to be the more preferred
ones in which possibly the user is strongly interested, while items with 1
star ratings may represent the user’s unsatisfaction. Therefore, numerical
values in explicit feedback can be a good indicator of the users’ preferences,
while a numerical value in implicit feedback such as the time spent on a
particular page indicates confidence. Lastly, the accuracy of approaches based
on the implicit inputs differs from that of the methods based on the explicit
ones [48].

In contrast to the above-mentioned models, which are already known as the
memory-based algorithms by the recommender systems’ community, the model-based
methods try to learn users’ preferences by using a certain recommendation
model according to the concerned issues [163]. At a high level, there are two
main major steps that need to be taken for this class of approaches. The first
step is to discover the relations between the items in order to build a model,
and second a list of top-N suggestions should be generated for a target user.
Different machine learning techniques are adopted in model-based CF, including
Bayesian networks [97], clustering models [99], latent semantic models [100],
Markov decision processes (MDPs) [164], association rule-based, and
dimensionality reduction techniques [21]. The work proposed by Miyahara and
Pazzani [96] is one of the Bayesian-based models, which consists of two
variants of collaborative filtering and shows a better performance improvement
over a simple similarity-based CF. In this study, on the basis of the
assumption that independent features are given to the class, the class with
the highest probability was considered the predicted class. Clustering is
another solution for CF-based techniques. For instance, K-means as a
clustering method divides the data into the different clusters in which the
entities in each cluster are similar to each other, while they are different
from those of the other clusters [165]. In addition, MDP is another popular
model adopted by the CF-based model. MDP-based CFs can be seen as a sequential
optimisation problem by applying Markov decision processes [164]; in this
problem, an agent needs to take the best action according to the current
state. Next, the association rule technique is a data mining technique which
searches for the co-purchased items to be analysed in order to find items
which are more probable to be purchased together in a customer transaction
history [166]. The association rule-based CF tries to discover the
correlations between items and then recommend the strong associative items to
the users [167]. CDAE is another recent work of the item-based CF model in
which a denoising auto-encoder is used for obtaining a better top-N
recommendations [168]. A user-item interaction in CDAE is considered a
corrupted version which needs to be recovered during the training process.
Because of the significant advance of attention mechanisms in neural
representation learning, a Neural Attentive Item Similarity Model (NAIS) [169]
was proposed very recently with an online personalisation item-based CF model.
The key idea behind NIAS is to find the more important items according to the
users’ preferences.

The accuracy of recommendations highly depends on the U-I matrix, which may
suffer from the data sparsity problem because of a lack of available
information in real-world scenarios. In order to tackle the data sparsity
problems, most of the CF-based recommender systems resort to the use of
additional information to enrich their data, and this may generate a new line
of research on RSs, called data enrichment in this thesis. Approaches in this
category seek to use more available information in order to augment their
data. Basically, there are different types of data which can be used as side
information and can be placed into three categories according to the source of
information: (i) item-based features, (ii) user-based information, and (iii)
interaction-based [103], [104], [170]. This auxiliary information can be
integrated into the user-item matrix in order to enrich this matrix for
obtaining a better prediction. Meanwhile, item-related attributes such as
content, category, and price can be added to the model to better describe the
items’ features. Moreover, user-based features can be divided into three main
classes of information: as (a) user demographic features, (b) user contributed
information, and (c) social networks information. The first class is user
demographic features including age, gender, personality and attitudes,
behaviours, and location, which can help a recommender system to build an
accurate user profile and make an accurate recommendation, accordingly [105].
Incorporating user demographic features into an RS cannot only result in
performance improvement but also better solve the cold-start problem [106].
Among all of the users’ demographic features, personality as a consistent
behaviour pattern with the strong relations with users’ preferences has
attracted more attention where there is an exclusive group of methods called
personality-based recommender systems, which is discussed later in this
section.

The second class of user-based information is user contributed data, including
free-text reviews/comments, multimedia content, tags, and geo-tags [171],
[172], [170]. Tags are short textual labels assigned to the items by the users
and can describe an item’s properties [173]. Tagommenders [174] is a good
example of using the extracted information from tags. Based on the results of
Tagommenders, tags can help recommender systems find the most important
dimension of items. Moreover, social media platforms such as Flickr
666http://www.flickr.com/. and YouTube 777https://www.youtube.com/ provide too
many options for their users, such as sharing, posting, and liking images and
videos. This opportunity can facilitate recommender systems to be aware of the
categories of interest of photographs and videos and thus, may generate an
accurate suggestion [22].

As exploiting tags as the extra source of information is beneficial for RSs,
there is a separate line of research known as tag-based RSs, which was out of
scope of this thesis [175]. Geo-tags provide explicit latitude and longitude
coordinates, which can provide the location information for RSs. The geo-tags
of a shared photograph can be used to trace the location of the users. The use
of these kinds of information can facilitate a location recommendation, which
was again out of the scope of this PhD thesis [176]. Since the introduction of
the Web 2.0 technology, people have been free to share their contents, such as
videos, text, and images. Exploiting these shared contents on OSNs can create
an opportunity to determine what type of items a user is interested in. These
contents can also be semantically and sentimentally mined to better construct
users’ profile and learn their preferences [170], [177], [171]. Recent
techniques, such as image processing, natural language processing, and object
detection, can contribute to the exploitation of more information and make
personalised recommendations to the users [133].

Last, but not the least, the third subclass of user-based information is
social network information, which can be provided by the relationships between
two users in OSNs, such as trust relationships and ‘following’ relationship
between two users in OSNs [178], [179], [180]. RSs can benefit from social
information from different aspects, such as the social properties of users,
social ties, communities, friendship, and social contents. For instance, the
strong social ties which can be calculated by using some factors such as
intimacy, emotional intensity, and amount of time, can be incorporated into
the RSs [181]. Trust can be defined as ‘Trust provides information about with
whom we should share information, from whom we should accept information and
what considerations to give to information from people when aggregating or
filtering data’ [182]. TidalTrust [183] and MoleTrust [184] are the two the
most well-known models in this domain that show that incorporating trust
information in discovering the best set of neighbourhoods may improve the
recommendation accuracy. TrustWalker [185] is another example of integrating
trust information with the classic item-based CF, which shows a better result
than the item-based CF.

Context is another information which can help RSs to better understand users’
preferences. There are different definitions of context in various domains,
such as computer science, information retrieval, cognitive science,
linguistics, philosophy, social science, and psychology [186, 187, 188],
[189]. According to Oxford Advanced Learner’s Dictionary, context is ‘a
situation in which something happens and that helps you to understand it’
[190], and according to Webster’s Dictionary ‘context is a situation in which
something happens: the group of conditions that exist where and when something
happens’ [191]. From the computer science point of view, context is defined as
‘everything’ that ‘affects the computation except the explicit input and
output’ [192]. The concept of context was introduced in the computer science
field in the late 1980s [193], and Schilit et al. [194] advocated the
introduction of context-awareness in computing in 1994. Among the different
aspects that can be considered a context in recommender systems, time,
location, and social relations have been shown to have great success in the
literature. LARS is a location-aware RS, which exploits users’ location to
find the nearest items, such as restaurants, bars and cinema halls [195],
[196]. Time is defined as ‘a non-spatial continuum that is measured in terms
of events that succeed one another from past, through present to future’ or
‘the measured or measurable period during which an action, process, or
condition exists or continues’ [191]. Time is another dimension of context
which can help the system to track the evolution of users’ preferences over a
period of time. Zimdars et al. [197] considered taking time information as a
context into their model. Baltrunas et al. [198] considered morning and
evening as a time of day factor and workday and weekend as a time of year for
having an understanding of users’ preferences for a particular period of time.
In another study, seasons (e.g. fall or winter), day of week (e.g. Saturday or
Sunday), and time of day (e.g. morning or midnight) were treated as a time
factor in RSs [199]. Finally, besides users and items information,
interaction-based data are the last type of side information, which refers to
a set of associated information that comes along with the items that a user
directly interacted with, such as the time of purchasing items [200] and the
location where a mobile application was downloaded [201].

Personality-Aware Recommender Systems. As we mentioned earlier, personality as
one of the main characteristics of users has attracted more attention, and
thus, we decided to discuss this class of approaches separately. Personality
is described as a ‘consistent behaviour pattern and interpersonal processes
originating within the individual’ [202], which can explain the wide variety
of human behaviour. Personality is known as the most important factor that
plays a significant role in people’s decision-making process [56]. People
having a similar personality type tend to share similar tastes. According to
Rentfrow et al. [203] people having the ‘reflective’ personality type with a
high score of openness really like to listen to the jazz, blues, and classical
music, while ‘energetic’ people with a high degree of extraversion and
agreeableness usually prefer rap, hip-hop, funk, and electronic music. In
addition, according to a study by Chausson [204], comedy and fantasy movies
are two interesting genres of movies for people who have the openness
personality type, while conscientious persons are more interested in action
movies, and romantic movies are the favourite types of movies for the group of
neurotic people.

This observation motivates RSs to integrate this important factor into
consideration. As illustrated by Braunhofer et al. [205], among all of the
users’ characteristics, personality is the most effective one. Personality can
be acquired by RSs either explicitly by filling out a questionnaire or
implicitly by observing users’ behavioural patterns. From the psychological
point of view, personality is the main individual characteristic which can
explain ‘patterns of thought, emotion, and behaviour’ [206]. Moreover, one of
the key properties of personality is that it is a stable behavioural pattern
which humans tend to show irrespective of their situations. There are several
personality traits models that can explain human behaviours, and among them,
the Five Factor Model (FFM) or the Big Five Model has drawn more attention in
both psychology and computer science research. FFM is ‘the dominant paradigm
in personality research, and one of the most influential models in all of
psychology’ [207]. On the basis of the FFM, people’s personality types can be
categorised into five main traits, briefly called OCEAN [109].

  * •

Openness to Experience: creative, open-minded, curious, reflective, and not
conventional;

  * •

Conscientiousness: preserving, organized, and responsible;

  * •

Extroversion: assertive, amicable, outgoing, sociable, active, not reserved or
shy;

  * •

Agreeableness: cooperative, trusting, generous, helpful, nurturing, not
aggressive or cold;

  * •

Neuroticism (Emotional Stability): relaxed, self-confident, not moody, easily
upset, or easily stressed.

One of the main difficulties of personality-based RSs is how to identify the
users’ personality type. Basically, there are two different ways of assessing
people’s personality types which can be can be grouped as follows:

  * •

Explicit techniques (filling out a questionnaire according to the chosen
model);

  * •

Implicit techniques (observing users’ behavioural patterns and then applying a
regression/classification model).

There are several questionnaire types based on the FFM model, for instance
NEO-Personality-Inventory Revised (NEOPI-R, 240 items), in which the
participants’ personality types are revealed after they answer several
questions [107]. For instance, Hellriegel and Slocum [208] proposed 25
questions for a questionnaire in order to calculate each of the five
dimensions of the FFM model. The score for each of these five factors was
computed as the average score on the five associated questions to that
particular dimension. If we take the ‘Openness to Experience’ dimension of FFM
as an example, the questions were based on assessing the ‘imagination’,
‘artistic interests’, ‘liberalism’, ‘adventurousness’, and ‘intellect’
properties of a person. For instance, users answered questions which started
with ‘I see Myself as Someone Who…’. The answers were based on a five-point
Likert scale, ranging from ‘strongly disagree’ to ‘strongly agree’. Besides
the explicit ways of identifying users’ personality types, user’s personality
types can be acquired implicitly by analysing their activities, such as
written review texts, and posts from online social networks [209], [210]. For
instance, Quercia et al. [211] analysed the user generated contents on micro-
blogs and showed the capability of these contents to represent the user’s
personality types. The authors used the M5 rules regression as a machine
learning tool to predict the users’ personality types. In another study, the
users’ records of Facebook activities were collected by Kosinski et al. [212],
[213], and then, logistic regression was applied to predict the peoples’
personality type. Although personality detection with questionnaires may
reveal a better understanding of a user’s personality, it is a tedious and
time-consuming task, and thus, users may be unwilling to undertake it. In
contrast, implicit personality detection models users’ digital footprints, and
their behaviours and actions are exploited to be analysed with no extra burden
on them [214].

This observation has inspired some RSs to incorporate users’ personality types
as the side information into their model in order to boost their performance.
Furthermore, aggregating the users’ personality type cannot only help RSs to
better deal with the data sparsity problem but also provide users with
suggestions even in a cold-start scenario [205]. For instance, Hu and Pu [1]
detected the users’ personality types by providing a questionnaire to the
users and asking them to answer the questions; their results showed the better
performance than the purely collaborative filtering methods. TWIN is another
example of integrating personality in a recommender system, which calculates
the user personality profiles on the basis of the NEO-Personality Inventory-
Revised classification scheme (also known as the Big Five model) [2].

Personality as a domain-independent concept along with individuals can also be
used in a wide range of domains, such as music [12], movies, and books [215].
Therefore, some RSs try to adopt this factor into their model to not only help
users with a diverse set of items [111] but also provide a better group
recommendation [112], [27] and improve the accuracy of RSs for music, movies,
e-learning, and web searches [216], [113], [57]. In addition, the users’
personality types can be seen as an important context that can be used in a
Point Of Interest (POI) recommendation as well [217]. South Tyrol Suggests
(STS) is an example of a context-aware POI recommender system which detects
the users’ personality type explicitly [217], [218]. As personality is a
domain-independent factor, it can be used in cross-domain recommenders as
well. Cantador et al. [219] investigated the impact of users’ personality
types on different domains. According to another study [57], ‘salsa-music
lovers are dissimilar to science-fiction-books lovers or news-tv-show lovers
are similar to mystery-books lovers’.

Thus far, we have discussed the benefits of incorporating personality factor
into a recommender system. Beside these advantages, personality can offer a
diverse set of items for users to discover unexpected items [220], [221].
Brynjolfsson et al. [222] investigated the effect of different personality
types on selecting diverse items. To do so, a survey with 181 subjects was
conducted for users to choose their favourite movies. The individuals also
needed to answer the questions to measure their personality types. Then, the
researchers investigated the correlation between diversity and personality and
realised that ‘reactive, excited and nervous persons (high in Neuroticism)’
like to select movies from diverse directors, while ‘suspicious/antagonistic
users (low in Agreeableness)‘ prefer movies from different countries.

Basic Deep Neural Network Models for CF. In the past few years, numerous deep
recommender systems have been proposed to model complex user-item relations
[78]. In particular, dealing with different types of data such as textual data
(e.g. comments and tweets) [223], [224], [225], [226], image data (e.g. shared
photographs and item images) has become a challenging task without the use of
neural network paradigms. Therefore, most of the existing RSs which are based
on the linear methods have become less attractive; consequently, RSs cannot
take advantage of more general and abstract representations. As presented by
Zhang et al. [78], the model proposed by Salakhutdinov et al. [227] is the
first neural network-based RS which uses Restricted Boltzmann Machine (RBM) as
the core. Neural Network Matrix Factorisation (NNMF) [228] and Neural
Collaborative Filtering (NCF) [229] are two representative works in CF which
use Multiyear Perceptron (MLP). DeepFM is another neural network-based
approach in which a deep component and a factorised matrix are jointly trained
[230]. While the use of MLP is a very straightforward solution, the other
neural network techniques, such as CNNs and Auto Encoders, may be more
expressive and highly efficient at identifying the main features of users and
items. AutoRec [231] and its extension, CFN [232], are examples of
AutoEncoder-based models which try to reconstruct their input data in the
output layer. The main difference between CFN and AutoRec is that CFN resorts
to the additional information about users and items in order to mitigate the
data sparsity problem, boost the prediction accuracy, and improve the model
robustness. CNN is another viable deep neural network which is good at
capturing local context. ONCF [233], an outer product-based neural
collaborative filtering (ONCF) framework, is an improved version of NCF. ONCF
introduces the interaction map term, which uses an outer product operation in
order to capture the pairwise correlations between the embedding dimensions.
Next, the result of the interaction map is passed to the CNN in order to learn
the high-order correlations among the embedding dimensions. Besides the
mentioned models, Graph Convolutional Networks (GCNs) is one of the most
prominent deep learning models with the core principle of iteratively learning
to aggregate feature information from local graph neighbourhoods [234, 235,
236, 237, 238, 239].

Advanced Deep Neural Network Models for CF. In recent years, the advancement
of attention mechanisms for focusing more on the main features of the inputs
has become more attractive in a wide range of domains, such as computer vision
[240], natural language processing [241], [242] and speech recognition [243],
[244]. In this context, among all types of deep learning techniques, CNNs and
RNNs are the two commonly adopted architectures which can be used with
attention mechanisms [245]. For example, Seo et al. [246] used a CNN owing to
its significant capability to capture complex features of users and items
properties by aggregating review texts. Moreover, the authors used dual local
and global attention in their model to provide a better-learned representation
of users and items. ACF [247] is another attention-based CF model which
consists of a two-level attention mechanism: the component-level and item-
level attention modules which can automatically calculate the scores to the
two levels of feedback in a distant supervised manner. Attentive Contextual
Denoising Autoencoder (ACDA) [248] is an attention-based CF model in which the
context of user-item interactions is incorporated by using attention
mechanisms. ACDA is an augmented personalised recommendation model based on
the denoising autoencoder. CML is another attention incorporated model for CF
proposed by Tay et al. [249]. The main principle of CML is its operation in
the metric space, as it tries to minimise the distance between each user-item
interaction in the Euclidean space. Here, an attention mechanism is applied to
generate the latent relation vectors over an augmented memory module.

####  2.2.3 Hybrid-Based Recommendations

This category of RSs is a combination of the content-based and collaborative
filtering-based RSs. Hybrid-based RSs take advantage of the benefits of both
methods, simultaneously, in order to increase the accuracy of RSs. According
to [20], there are three different ways in which these methods can be
combined. The first is to use the models that use collaborative filtering and
content-based methods separately and then combine their predictions. The
second type of hybrid models includes methods that incorporate either content-
based or collaborative filtering characteristics into another model in order
to make a hybrid recommendation. The third class of hybrid approaches combines
both collaborative filtering and content-based recommendations into one single
model in order to benefit from the advantages of both the models as a unified
recommendation system. Next, we will review these three classes of hybrid
recommendations models in detail.

##### Combining Separate Recommenders

The very first step of approaches in this class of hybrid recommender systems
is to separately construct collaborative and content-based recommender
systems. Then, the final result of an RS can be generated either by combining
the ratings from both individual recommenders [250] or by selecting one of the
recommenders which generates more accurate results [39]. For example, a hybrid
recommender system proposed by Tran et al. [251] switches between two
different types of recommendation engines by using the Interactive Interface
Agent (IIA). IIA is responsible for choosing the more suitable subsystem for
service according to the user information and behaviours.

##### Adding One Individual Recommender to Another One

In the previous section, we saw that content-based RSs are successful in
discovering items similar to those that a user was interested in before, in
terms of their descriptions. Although, content-based RSs have shown a
significant development in the mentioned task, CF-based studies may perform
better because of several reasons. First, there is no need for CFs to analyse
the items’ descriptions, which may need a complex Natural Processing Technique
(NLP) in order to provide an accurate prediction. Second, CF-based systems can
generate a set of suggestions that are relevant for a user, but without the
information from the user’s profile [252], [149]. On the basis of these
observations, integrating users and items characteristics in a CF-based model
is a more common and successful way to make a hybrid recommendation, which
takes the advantages of both models and reduces their limitations.

Fab is one of the first examples in this category of hybrid methods, which
detects similar users who have similar website preferences by exploiting the
content of users’ profiles [253]. Furthermore, a PTV system uses content-based
techniques to analyse information about TV programs, and then, finds users who
have the same preferences on TV shows. Next, it uses both content-based and
collaborative filtering-based algorithms for making recommendations [254].
LIBRA, a book recommendation engine, is another example of a hybrid-based
recommender system. LIBRA collects information from
Amazon.com888https://www.amazon.com/ in order to make suggestions. Good et al.
[255] proposed a hybrid recommender system integrating multiple filter bots,
which are learning agents, in order to help the collaborative filtering
community make better recommendations. In addition, Content-Boosted
Collaborative Filtering (CBCF) [256] uses the contents of the items of
interest to overcome the data sparsity problem, which by itself is a
challenging problem for CF models.

Incorporating items’ characteristics also can help this model to convert a
sparse rating matrix into a full rating matrix. This solid framework can
generate more accurate recommendations than the purely content-based or CF-
based systems. Most of the mentioned hybrid models take advantage of using
content-based approaches in CF systems. While there are other attempts in the
literature to use the dimensionality reduction technique, latent semantic
indexing (LSI) is used to determine a set of neighbourhoods by analysing user
profiles [250].

RS |  Input |  Working mechanism |  Pros |  Cons
---|---|---|---|---
Content-based |  User characteristics , Item descriptions |  Recommend items similar to those that a user has liked before |  Simple and straightforward, can deal with cold-start problem |  Over-specialisation, limited content analysis
Collaborative Filtering |  User-item rating matrix |  Recommend items similar to those that a user’s neighbours have liked |  Can recommend serendipity items, effective and simple |  Suffer from data sparsity and cold-start problems
Hybrid |  Item and user characteristics and their interactions |  Combines two or more types of recommendation strategies |  Benefits from the advantages of both content and collaborative -based approaches |  Relatively complex to develop
Table 2.1: Comparison of different models of general recommenders.

##### Developing a Single Unifying Recommendation Model

Recently, among all the types of hybrid RSs, approaches that fall into this
category have attracted more attention from researchers. According to [257],
hybrid-based recommenders are usually based on ‘probabilistic methods such as
genetic algorithms [258], [259], fuzzy genetic [260], neural networks [261],
[262], [263], Bayesian networks [264], clustering [265] and latent features
[266]’. For instance, a Bayesian preference model was proposed by Ansari et
al. [267], which made use of four types of information, such as a person’s
expressed preferences, preferences of other consumers and experts, items’
characteristics, and individuals’ characteristics. Their results represented
an improvement in prediction ability compared to the RSs modelled only on
collaborative filtering or content-based approaches. In another work, the
authors used all the available information, such as users’ ratings, users’
features, and items’ features in a single probabilistic unified framework to
construct a hybrid recommendation system, which could address the cold-start,
scalability, and sparsity problems [268].

###  2.3 Sequential Recommenders

With the rapid growth of online platforms, many companies have started
building their e-commerce websites and smartphone applications to encourage
their customers to keep interacting with products and services. These
platforms can be extremely helpful for users to narrow down their options,
while a huge amount of interaction information can be generated. For instance,
around 62 million trips with Uber999https://www.uber.com/au/en/ were recorded
in July 2016 [8]. By analysing the huge amount of information about users’
historical sequential behaviours, Sequential Recommender Systems (SRSs) can
predict the next interacted items. This can help users by easing their
decision-making processes as well as increasing business profits for
companies.

All the aforementioned traditional RSs mostly treat the recommendations task
as the matrix completion problem, in which a sparse user-item rating matrix is
given as an input. As already discussed in the previous section, this type of
RSs (general recommenders) has been generally well-studied and different
techniques have been applied in this setting. The main job of general
recommenders is to predict the missing ratings value, which can represent the
general users’ preferences by itself (i.e. long-term users’ preferences).
While traditional approaches are good at modelling the users’ general taste,
they usually ignore the short-term users’ preferences, which results in
missing interest drifts of individual users over time [40]. We present a
general framework of a sequential recommender system in Figure 2.5.

Figure 2.5: General framework of a sequential recommender system.

As is clear from the Figure 2.5, in a sequential recommender, the most recent
user’s activities are taken into account. Take Jimmy’s shopping basket for
example: a set of his current actions at time $t$ is passed to a sequential
recommender. According to his transactional history at time $t$, Jimmy first
looked for a coffee machine and added one into his wishlist for further
shopping. Then, he purchased a set of double-walled glasses and cinnamon
powder. Next, he made his final action at time $t$, by clicking on an
electrical kettle. By analysing Jimmy’s current behaviours, a sequential
recommender system can realise that Jimmy is interested in coffee-related
items. Hence, according to a sequential recommender, a box of coffee capsule
may be a good suggestion for him.

In contrast to the traditional RSs, Sequential Recommender Systems (SRSs) have
emerged as a new line of RSs which differ from traditional RSs in terms of
their inputs, outputs, computational tasks, and abstract problem
characterisation [41]. Here, a set of purchased items in one basket can be
considered a session, which can be used as an input. In contrast, SRSs take a
sequence of user-item interactions to predict a set of next interacted user-
item interactions, which is the SRSs’ outputs. In general, the main purpose of
SRSs is to model the sequential dependencies among user-item interaction
sequences and discover the hidden information embedded from one sequence into
another sequence. SRSs have attracted the attention of researchers on
different applications such as POI recommendation [42], [43], music
recommendation [44], [45], and browsing recommendation [46].

Despite the significant improvement of SRSs from their emergence, there have
been just a few attempts to categorise the existing studies in SRSs [269].
Therefore, in this thesis, we have divided the current studies in SRSs from
different points of view. As the basic unit of a sequential recommender system
is a session, we will first start categorising SRSs regarding to the sessions’
complex characteristics [67]. Then, a summary of SRSs with respect to their
adopted models will be given in Section 2.3.2. Next, we will provide a
division based on the SRSs’ input types in Section 2.3.3.

####  2.3.1 SRS Categorisation in terms of Session Characteristics and
Challenges

In this section, we aim to introduce the different characteristics of a
session, which may bring various challenges in a sequential recommender
system. We will discuss each of the related issues with the possible
solutions, separately. As stated by Wang et al. [67], users’ shopping
behaviours may not follow a certain pattern and may have complex
characteristics which need to be solved. Therefore, we can divide the current
studies on SRSs with respect to the main characteristics of each session of
user-item interactions, such as dealing with the noisy items in a session,
capturing a session’s purpose, handling the items’ order in a session, and
dealing with the long user and item interaction session. In the following
sections, we will comprehensively review all of them.

#####  Dealing with Noisy Sessions

Usually, people’s decision-making process is not very certain. Take a customer
shopping basket for example: she/he may have a shopping list and collect all
of the items on her/his basket, but at the last second of the shopping, she/he
may make an instant decision and add several other items to her/his basket,
which are totally different from what she/he has purchased already. The last-
minute purchase may generate interference, which makes dependency modelling
more complicated. While it is an important problem that needs to be solved, it
has not been sufficiently studied as yet. Inspiring by the great capability of
the attention mechanism in context learning, ATEM was proposed by Wang et al.
[84]. ATEM applies the attention mechanism to discover the transactional
context and pays more attention to the contextual items which are highly
related to the next interested items and downgrades those which are not too
relevant. An attention layer in ATEM is responsible for automatically
calculating a different score for the contribution of different items. Thus,
ATEM can avoid interference from irrelevant items in a long context. Another
solution for dealing with a noisy session is memory networks. Because of the
strong capability of external memory networks (EMN) [270], [271], [272] for
storing historical hidden states, they are used in a wide range of tasks such
as question answering (QA) [273], natural language transduction (NLT) [274],
and knowledge tracking (KT) [275]. Memory-Augmented Neural Network (MANN) was
proposed by Chen et al. [85] to use EMN in their system in order to make
personalised recommendation scenarios. By doing so, MANN could use a memory
matrix for storing the previous states instead of merging them. In particular,
they proposed two versions of their framework, namely item-level and feature-
level Recommender system with external User Memory networks (RUMs), to better
model users’ behaviours and improve the recommendation performance,
accordingly.

##### Capturing a Session’s Purpose

People usually start buying items with a specific intention in their mind.
Take online shopping for example: a user may want to make a pizza. Considering
this purpose, she/he may take successive actions, such as purchasing a pizza
base, clicking on a pizza sauce, and browsing for a light cheese. During these
actions, the user may interact with some unrelated items because of curiosity,
and thus, if sequential recommenders neglect to take the user’s main intention
into account, false dependencies will be modelled, leading to inappropriate
suggestions. Therefore, it is an important job for a sequential recommender to
capture the user’s main purpose. Neural Attentive Recommendation Machine
(NARM) is a sequential recommender in which the user’s main purpose in the
current session is emphasised [10]. NARM consists of two encoders, namely a
global encoder and a local encoder. The global encoder is responsible for
summarising the whole sequential behaviour, while the user’s main purpose in
the current session is captured by the local encoder. Both the encoders use
RNN with GRU as the basic component, and the recommendation scores for each
potential item are calculated by using a bilinear matching scheme. STAMP,
proposed by Liu et al. [276], is another effective solution for showing the
importance of taking users’ purposes into account. These researches showed
that the user’s intention may be more in response to the current action. STAMP
proposes a novel short-term attention/memory priority model, which considers
the users’ recent preferences from the last-clicks. Moreover, STAMP introduces
an action priority mechanism to pay attention to the both long- and short-term
user interests. As stated by the authors, STAMP is simpler and faster than
NARM, which makes it more applicable in real world scenarios. Mixture-channel
purpose routing networks (MCPRNs) is another work in this setting, proposed by
Wang et al. [277], which assumes that a session may consist of several
purposes. Recently, ASLI [278] was proposed as a sequential recommender system
in which self-attention is used to identify similar items in a user-item
interaction session in order to capture the users’ hidden intents. MCPRNs
introduce a purpose routing network to discover the reason behind the
appearance of each item and then, assign each item to its corresponding
channels. Next, all the channels are integrated into a multi-purpose session
to rank the candidate items. While capturing the users’ main intention has
shown its potential in SRSs, it is still in its early stages.

##### Handling Items’ Order in a Session

In the real world, it is more probable to purchase a set of products with no
ordered assumption among them in the shopping basket. As stated by [279], most
of the grocery shoppers may just add products into their cart by random
selection, which does not follow any strict order between them. For instance,
it does not really matter whether users put bread or butter first into their
shopping a bottle of jam. Moreover, they may suddenly be attracted by some
items and then pick them up and add them into their basket. Therefore, it may
be possible for a sequence of purchased items to relax the ordering assumption
in order to have flexible user-item interaction sequences. While it is a
simple and efficient observation, little attention has been paid to this
effective fact by the current studies. The existing techniques on SRSs such as
RNN, MC, SPM, and factorisation machines may not be good at modelling
unordered items in a sequence, as point-wise dependency relationships are the
key principle behind these models. However, although pattern/rule-based
approaches are simple and effective techniques, they are suitable options for
modelling a flexible session. The main intuition of these models is to
discover frequent patterns and then use them for making further
recommendations for other users. For instance, a general-purpose Semantic Web