© Higher Education Press and Springer-Verlag Berlin Heidelberg 2022 Received
August 23, 2022

#
$\bm{A~{}Survey~{}of~{}Recommender~{}System~{}Techniques~{}and~{}the~{}E-commerce~{}Domain}$

Imran Hossain  Md Aminul Haque Palash  Anika Tabassum Sejuty  Noor A Tanjim
MD Abdullah AL Nasim  Sarwar Saif  Abu Bokor Suraj  Md Mahim Anjum Haque
Nazmul Karim  Department of Research and Development, Pioneer Alpha,
Bangladesh

###### Abstract

In this big data era, it is hard for the current generation to find the right
data from the huge amount of data contained within online platforms. In such a
situation, there is a need for an information filtering system that might help
them find the information they are looking for. In recent years, a research
field has emerged known as recommender systems. Recommenders have become
important as they have many real-life applications. This paper reviews the
different techniques and developments of recommender systems in e-commerce,
e-tourism, e-resources, e-government, e-learning, and e-library. By analyzing
recent work on this topic, we will be able to provide a detailed overview of
current developments and identify existing difficulties in recommendation
systems. The final results give practitioners and researchers the necessary
guidance and insights into the recommendation system and its application.

###### doi:

: 10.1093/comnet/xxx000

††articletype: REVIEW ARTICLE

Recommendation Techniques , Recommender System , Collaborative Filtering,
Scalability , E-commerce.

##  1 Introduction

Recommendation systems are software devices and techniques that make
recommendations to users for suitable and interesting products. The
recommendations apply to different processes of decision-making, such as which
items to purchase, which news to read, or which music to listen to. The
recommendation has become more and more relevant and has changed the way of
interaction between users and websites. Recommendation engines are developed
to recommend songs, products, films, television shows, and a variety of other
content that involves some degree of choice. The fundamental function is to
define users’ needs and suggest them based on what other users have looked at,
are interested in, and enjoyed. Recommendation systems have a wide range of
applications in several fields, such as economics, e-commerce, education, and
scientific research papers [71].

The expeditious development of IT is rapidly increasing the amount of data.
Recommender systems use information processing to suggest user-interesting
information, which can be defined as the process that recommends a suitable
product after knowing the expectations and wishes of the customer [33]. Most
of the literature on recommendation systems focused on improving
recommendation technique’s reliability. They usually contain input data,
background data, and an algorithm that combines input data and background data
to provide a recommendation. Therefore, it saves time and unnecessary effort
in finding products, and they will recommend the right products to purchase
[19]. Such systems are also particularly useful for recognizing the user’s
preference.

Recommendation systems help users navigate through website data by making
suggestions based on their preferences [32]. Developing recommender systems
should be an effective solution to help customers quickly pick items based on
their interests. To provide the user with a more effective search environment
[27], they combine concepts of machine learning, information retrieval, and
user recognition. Such systems are designed to determine user interests,
behaviors, and help them get what they’re searching for or find useful in a
large amount of data.

In all e-commerce, e-tourism, e-government, e-government software, and
information management systems, recommender systems are mostly used.
Recommender systems are responsible for providing specific users with accurate
and reliable data. It is a serious business system that reshapes the
e-environment world. Most major e-commerce companies employ recommendation
systems to help their customers find items they may be interested in buying
quickly, leading to easier checkouts and more sales. Essentially, a
recommender program is an adaptive framework based on user experience and user
data analytics to define and serve the needs of the user. There are
collaborative and content-based, knowledge-based methods to carry out such an
analysis. This artificial intelligence explores huge amounts of user data,
including clicks, orders, ratings, etc., using various algorithms to make
recommendations [49].

Many research articles have been written over the past few years on
recommender systems. Such papers, however, concentrate on recommendation
techniques and methods or a particular area of development of recommendation
systems. Only the comprehensive study of recommendation systems applications
is discussed in the paper [44]. Since recommendation systems are playing an
important role in many areas, this paper aims at analyzing the current
situation of recommendation systems and discussing various forms of
recommendation techniques and system development in real-life software
applications.

Two types of articles have been reviewed and classified in this research:

  1. 1.

Articles based on recommender system techniques and related issues.

  2. 2.

Articles based on applications of recommender systems.

The selection process is based on the following criteria for choosing high-
impact and creative articles: 1) published recent year 2) published in
journals and conference proceedings which has a good impact factor. Such
papers are reviewed and chosen as follows:

  1. 1.

For selecting research papers on RSs for our review, the following journal
repositories were used: ACM Digital Library, IEEE, Elsevier, and Springer.

  2. 2.

Searching process was therefore carried out based on keywords relevant to RSs
in e-commerce, e-learning, and e-library, e-tourism, e-resources, e-government
applications, etc.

  3. 3.

Depending on the keywords, recommender system techniques such as content-based
filtering, collaborative filtering, hybrid approaches, context-aware, and
knowledge-based strategies separated these articles. Relevant papers were
examined and chosen for each RS method.

Finally, we selected 75 papers for our research on RS. The following section
of this paper is described as Section 2 introduces and categorizes the
recommendation system techniques. Sections 3–7 frequently discuss
recommendation systems in e-commerce, e-learning and e-library, e-tourism,
e-government, and e-resources domains. Section 8 discusses the challenge of
recommendation systems, and Section 9 provides comprehensive analysis.

##  2 Recommendation techniques

Using effective and appropriate recommendation systems is essential for a
platform that will provide its users with helpful and informative
recommendations. We divided the recommender system into i) content-based
filtering, ii) collaborative filtering, iii) hybrid filtering, iv) context-
awareness, and v) knowledge-based recommender techniques. Fig. 1 displays the
classification of recommendation techniques.

Figure 1: Classification of Recommender System

###  2.1 Content Based Filtering Technique

In this subsection, we discuss the content-based filtering technique.

The content-based system makes recommendations for those products that are
close to the products already used by customers in the past [9]. To generate
suggestions, the CB method conducts more assessments of the item’s attributes.
In web pages, journals, product, and news recommendations, the CBF filtering
method is most effective.

The content-based system is an algorithm that depends on the domain to produce
predictions. It focuses more on analyzing the characteristics of products.
Using characteristics obtained from the content of products that the user has
assessed in the previous, recommendations are produced in content-based
filtering techniques oriented on customer profiles. The user has advised items
mostly linked to the favorably rated items. To produce significant
suggestions, CBF utilizes distinct kinds of models to discover similarities
between records. It does not take into account the attitude of customers
towards the product, which limits its recommendation accuracy [37].

Recommendations based on content treat recommendations as a user-specific
classification issue and train a classifier based on product characteristics
for the user’s likes and dislikes. Specifically, based on the characteristics
of the content, similarity metrics can identify the most similar items that
will be rated similarly [6]. It also proposes products similar to those that a
particular customer enjoyed in the list of item rates.

The goals of content-based methods depend on discovering correlations between
item content and user correlation, as with CF methods. The root of the
strategy based on content can be directly linked to the retrieval of
information [12]. Fig. 2 shows the process of CB filtering.

Figure 2: Content based filtering process

A commonly used algorithm is the representation of tf–idf. By using the
weighted vector of item characteristics, the scheme generates a content-based
user profile. The profile of the customer is built by evaluating user answers
to user navigation history. It indicates products with similar characteristics
to those chosen by the user in the past [54]. Simple methods use overall rated
item vector values, but other advanced schemes use machine learning techniques
such as Clustering, Bayesian Network, Artificial Neural Network, Decision
Tree, or Neural Network to predict the likelihood of the item [75]. In this
paper [30] author submitted a customized model based on a probabilistic
relational model. Furthermore, the CBF approach can still update its
recommendation in a short time if the user profile gets changed.

The main drawback of this method is the need for a deep understanding and
description of the characteristics of the products in the profile. However, by
pairing CB with the CF filtering strategy and other methods, these
shortcomings can be resolved.

###  2.2  Collaborative filtering technique

In this subsection, we discuss the collaborative filtering technique.

Collaborative recommendations are most familiar and commonly applied,
particularly in e-commerce [9] and one of the most effective methods used in
the recommendation [34]. Collaborative filtering techniques generate user-
specific suggestions for products based on rating or buying items without
requiring explicit data on either products or users. Collaborative filtering
depends on the user rating to construct a matrix of user-items based on the
ratings of comparable customers with the same or related interest preferences
[35]. By calculating user profile similarities, the system then finds the user
with similar feelings and preferences, building a comparable user group called
neighborhood [15]. CF is a well-established technique that performs well.

As such, CF enables the user to give ratings on a collection of components in
such a manner that, if sufficient data is collected in the system, suggestions
can be made to every user based on data supplied by those users that we think
to have the most in common with them. Collaborative filtering is an excellent
field of study. It depends on a rating database, then the scores are compared
to the customer who gives the request [55]. CF-produced recommendations can be
either predictive or recommended. Prediction is the value of statistics. Since
Recommendation is a list of the top N items that the user will like the most
as seen in Fig. 3.

Figure 3: Collaborative filtering process

The CF method is split into two classifications: memory-based and model-based
[8].

####  2.2.1 Memory-based

The memory-based method identifies similarities between the active user and
other users using similar mechanisms such as Jaccard similarity, Pearson
correlation coefficient, Cosine similarity, etc. The missing rating of an
active user is then estimated, and the Top-K rated product is recommended to
the active user. The products that a user rated or bought in the past play a
significant part in looking for a customer who shares his perception [75].
Once a user’s neighbor is discovered, it is possible to use distinct
algorithms to combine neighbor preferences to produce recommendations. They
have attained significant success in real-life apps due to the efficiency of
these methods. Memory-based CF is divided into two classifications: user-based
and item-based.

  * •

User based collaborative filtering: User-based filtering technique computes
user similarity by using comparison with their preference to the same product
and calculates the preference for active user products [24]. Fig. 4 shows the
user-based CF techniques.

Figure 4: User based CF

  * •

Item based collaborative filtering: Item-based CF calculates items similarity
to make predictions. This technique first collects all items that active users
buy and then detects correlations between the items and the target item. It
then selects the top N items which are the most similar to determine the
choice of the active user for the target item [24].

Figure 5: Item based CF

Fig. 5 shows item-based CF techniques.

####  2.2.2 Model-based

Previous ratings are used in a model-based method to create a model using the
method of machine learning. Predictions can be produced for an individual
customer once the model is created. This method uses the past scores to create
a model to enhance the Collaborative Filtering Technique’s efficiency. Machine
learning or data mining methods can be used to develop the model. These
methods can rapidly recommend a set of products using pre-computed models and
have shown outcomes of recommendations that are close to those of
neighborhood-based recommendation methods. This is also known as a
computational intelligence-based recommender system. Clustering, Bayesian
network, artificial neural network, decision tree, or neural network are
commonly used in recommendation systems to construct recommendation models
[44].

###  2.3 Hybrid filtering technique

A hybrid filtering system generates better efficiency by combining two or more
recommendation systems. Most recommender systems use hybrid filtering that
combines content-based filtering, collaborative filtering, and other methods.
Hybrid methods can be applied in several ways by separating and combining
collaborative filtering and content-based filtering approaches into one model.
The hybrid filtering approach combines multiple recommendation strategies to
simplify the system and avoid certain limitations and problems with
traditional recommendation systems. Hybrid filtering systems that can
manipulate and control both user preferences and content have been developed
for this purpose [30].

The concept behind hybrid systems is that they can offer more accurate and
efficient recommendations than a single technique because another algorithm
can resolve the drawbacks of one algorithm and improve the performance of
recommender systems [32]. However, the hybrid approach’s efficiency is
superior to a single approach because it reduces the method’s disadvantages.
To this end, these systems combine different previous system recommendation
techniques to achieve some interaction between them [69]. Such systems have to
be closely designed so as not to inherit the drawbacks of the chosen system.

It is possible to combine CBF and CF in various ways [1]. Fig. 6 demonstrates
the techniques for calculating and subsequently combining CBF and CF
suggestions individually.

Figure 6: CBF and CF combines

In Fig. 7, the techniques that integrate CBF features into the CF strategy are
shown. Add CBF projections to the CF rating matrix. Then it modifies the
rating matrix entry by pairing it with the matrix produced by clustering the
products by their characteristics for the CF. Authors integrate personality
features into the measure of CF similarities to reduce the issue of new users
[22].

Figure 7: CBF and CF combines

Fig. 8 shows the techniques used to build a unified CBF and CF model. The
writers use Bayesian networks in other studies to combine the features of CBF
and CF and produce more reliable suggestions.

Figure 8: CBF and CF combines

Another hybrid method is a combination of characteristics where
characteristics obtained from distinct sources of information are combined and
supplied to a single algorithm for advice. This technique enables the system
to consider cooperative information without relying only on it, thus reducing
the system’s sensitivity to the number of customers who rated one item.
Instead, it enables the system to make resemblance data about products [62].

There are seven different types of Hybridization processes as Weighted,
Cascade, Switched, Mixed, Meta-level, Feature augmentation, and Feature
combination [24] that are described on table 1.

Table 1: Hybridization methods Hybridization methods |  Description
---|---
Weighted |  The decision is taken based on the score obtained for this hybrid method from a different recommender system. The result of each recommender system is combined in a single numerical component in order to determine the final list of recommendations.
Cascade |  In cascade, recommendation is based on a series of recommendations, One technique’s recommendations are refined through another recommendation technique. A list of suggestions is produced by the first recommendation method, which is improved by the next recommendation technique.
Switched |  This method can be used to select One strategy in the set of recommendation strategies available
Mixed |  The different recommended systems function together to provide the final personalisation list with a collaborative decision.
Meta-level |  For another recommendation system, the output of a single recommendation system is used as input.
Feature Combination |  Different features of the source of knowledge are combined to create a single domain.
Feature Augmentation |  One information source’s features are calculated to make it usable for any other recommender algorithm to function as an input

###  2.4  Knowledge based Recommendation Techniques

Knowledge-based recommendation systems are a specific type of recommendation
system focused on explicit knowledge of the variety of items, user
preferences, and criteria for recommendations. These systems are used in
circumstances where content-based and collaborative filtering cannot be used.

The knowledge-based recommendation provides users with items based on user
knowledge, items, and/or relations. In addition, a knowledge-based system
provides a practical knowledge base that determines how a particular item
meets the needs of a particular user, which can be done based on assumptions
about a user’s desires and suggestions. The knowledge base contains rich user
and item information that can help generate more intuitive and better
explanations for the recommended items [9].

Catherine et al. [11] demonstrated how the use of external knowledge in the
form of knowledge graphs can produce explanations. The proposed method ranks
items and knowledge graph entities in conjunction with their explanations,
using a Personalized PageRank procedure to produce recommendations. The paper
works on the case of film recommendations and generates a ranked list of
entities as explanations by ranking them together with the corresponding
films.

Unlike [11] who adopted guidelines and knowledge graph programming for
explainable advice, Ai et al. [3] suggested embedding information graphs for
explainable advice. In this study, the writers have created a user-item
knowledge graph that includes a variety of user, product, and entity
relationships, such as user purchased items, category items, and items jointly
purchased, etc. The knowledge-based recommendation is based on various data,
such as a set of items, rules, or similarity metrics. Rules describe which
items should be recommended, based on the user’s demands. In terms of item
property requirements, the user expresses his requirements that are both
internally represented in terms of the rule. Fig. 9 shows the KB system.

Figure 9: Knowledge based recommendation process

###  2.5  Context-aware recommender system

Context-aware recommendation systems create more valid recommendations by
applying them to the user’s specific contextual condition. This recommended
system works on contextual information such as weather forecasting, daytime
weather forecasting, geographical location, and rain prediction. The context
approach is now used by most effective recommender systems to reliably utilize
information [14] [46].

A technique was suggested for extracting an emotional context automatically
from YouTube-based films related to personal comments in the paper [52].
Likamwa et al. [38]included speed, light, and other physical characteristics
in their affective recommendation system that addresses the person’s
behavioral status and provides a mood for involvement in context-aware
computation. A multimodal recommendation system was proposed that includes
contextual data, interaction data, and users’ affective behaviors and made
important suggestions for unseen videos [5].

The context recommendation has three primary methods: pre-filtering, post-
filtering, and context modeling [2]. These method are described on table 2

Table 2: CARS methods Approaches |  Description
---|---
Context Prefiltering |  Context is used to choose any data set and then predict as a traditional system of recommendations. Another way to do this is to separate items or users by context as though they were different items or users.
Context Postfiltering |  Ratings are predicted and, using the context, the results are filtered. This can be done by ordering the results according to the context or simply filtering the results
Context Modeling |  The context is used in the model right. It is more complex, and multiple machine learning models such as SVM, matrix factorization, or a markov chain could implement it.

##  3 Recommender system in e-commerce domain

E-commerce has grown quickly in this Internet era, enabling millions of
products to be sold. E-commerce customers suffer from having to choose items
from millions of items. Recommender systems have become the most common method
in the e-commerce industry to handle information or data overload problems. A
recommendation system assists the customer in selecting products from millions
of items. RS is currently being used on nearly every e-commerce website,
helping millions of customers.

Recommender systems have helped many e-commerce platforms like Amazon.com,
Netflix, Pandora, Last.fm, flikart.com, e-bay, and so on to boost their
profits over the past century by using multiple Recommender systems methods.
The outcome of this system is used by both companies and users of e-commerce.
The recommender system not only helps the customers to get the desired product
but also increases the company’s income by selling more products [58].

For e-commerce services, the rating is a popular feature, particularly for
electronic products. In [26], a slope one algorithm was suggested that focused
on the combination of trustworthy users and data similarities using a rating
dataset. In order to fix the problem of improving the quality of
recommendation systems, Qian et al. [57] develop an emotion-aware RS focused
on hybrid information fusion in which three representative forms of
information are merged to evaluate the characteristics of the user
comprehensively user feedback data, social network data, and reviews are used
respectively as explicit, implicit, and emotional information.

Fuzzy methods are also used in recommender systems for CB e-commerce. For
products made up of distinct parts, Cao et al. [10] created a fuzzy based RS.
When purchasing a laptop, shoppers may regard each component’s personal
efficiency, such as memory, motherboard, CPU, etc. The weights of shopper
requirements on each element are collected and a fuzzy similarity model of the
measurement model is then used to produce the most satisfied applicants. In
the music recommendation system suggested to handle rich social media data, a
hypergraphic model has been developed to improve the use of rich social
information [64]. The user recommendations should be explained in such a
shopping assistant system. For instance, when buying expensive goods from
intelligent sales assistants, which are able to balance the different
requirements of the user, buyers expect to be skilfully pushed through the
options.

Real-time dynamic recommendation system for all website visitors, whether they
are registered or not registered [41]. A rational recommendation system based
on action is suggested, utilizing lexical patterns to produce recommendations
for items. The efficacy of the proposed system is assessed by collecting real-
time e-commerce data and comparing the system to item-based and user-based
techniques. This system provides good quality accuracy and minimizes the
traditional recommendation system’s limitations. A context-aware learning
system was developed to predict the rating known as CARL [70]. For a specified
user-item pair, CARL generates a joint representation based on latent
interactions and features of their individual. CARL then uses Factorization
Machines to model higher-order feature interactions, based on the user-item
pair to predict the rating.
Lin et al. [39] suggest a multi-level attention system to explore the
usefulness of reviews and the meaning of words and the MulAttRec Model for
Recommendation. They implement a hybrid prediction layer that models the non-
linear link between users and objects through the correlation Factorization
Machine to the deep neural network, which highlights both high order and low
order.

In short, for digital and physical products, e-commerce recommendation
platforms are frequently used in online purchasing. To use their new
algorithms, the researchers have developed several successful applications for
e-commerce systems. Such systems provide developers with good guidelines on
how to implement e-commerce recommender systems in practice.

##  4 Recommender system in E-learning and E-library domain

The use of e-learning and e-library resources has expanded significantly in
recent years. Such developments have resulted in a significant increase in the
availability of online learning and library services. With this increase in
resources for e-learning and e-library, it’s hard for learners to find useful
and relevant resources. Recommended systems can solve the problem of
information overload by filtering out redundant resources and choosing
appropriate resources automatically for individuals based on their individual
preferences.
A personalized suggestion model for e-learning materials (PLRS) was introduced
in [42]. Once a learning activity database is formed and the system obtains
information about the registration of a learner, the PLRS uses a model for
computational analysis to classify the learning requirements of an individual
and then deploys matching rules to produce a recommendation. A fuzzy item
response theory is introduced in a personalized recommender system for
courseware designed in [13] to initially gather the interests of a learner,
after which, as a percentage of their understanding of the training
courseware, the learner receives a fuzzy answer.
A hybrid KB recommendation system based on sequential pattern mining and
ontology to advise learners on e-learning tools [65]. In this proposed
recommendation method, ontology is used to design and describe the learner and
training resources’ domain knowledge, while the sequential pattern mining
algorithm discovers the sequential learning habits of the learners. This
hybrid approach will overcome both the problems of data sparsity and cold
starts by using the learner’s sequential access approach and ontological
domain knowledge.
A user-centric design approach to applying Web-based education systems with
personalized support was proposed and applied to the Willow system, [61]. This
study shows that the creation of learning personalized e-environments is a
process that takes into account the needs of learners throughout the life
cycle of e-learning. It also noted the use of the e-learning life cycle in
developing and evaluating personalization support through feedback on Web-
based education systems. A personalized online interface for learning objects
offering a comparison of the learner profile and learning object explanation
is proposed in the paper [7]. The comparison is based not only on the
principles of the qualities of the student profile and the explanations of the
characteristics of the learning object, but also on the significance of those
attributes.

A recommendation system in university digital libraries to recommend research
resources [56]. In these, a Fuzzy Linguistic RS was suggested where multi-
granular Fuzzy Linguistic Modeling has been used to represent and manage
flexible information through language labels, and a hybrid recommendation
system was presented that combines CF and CB methods. Users are allowed to
designate their expectations through an ambiguous fuzzy linguistic
preferential interaction to reduce the user feedback initiative. Huang et al.
[23] developed a self-adaptive website for an e-library recommendation system.
It is split into two parts: online and offline. The former consists of data
collection, preprocessing data, and periodic pattern mining. The latter
creates sets of recommendations by using the university library. To get the
current user access paths, a sliding window approach is used, and then the
aggregation tree-based association rule algorithm is used to produce an
association rule set. Finally, to get a recommendation set, you have to use
the recommendation set generation algorithm.

The hybrid recommendation methods that combine CB, CF, and/or KB strategies
are commonly used in the above-mentioned e-library recommendation systems. One
reason for using hybrid methods is to take advantage of the merits of various
recommendation strategies. KB plays a more significant role in making
decisions for e-learning RS systems than other techniques.

##  5 Recommendation system in e-tourism domain

E-tourism recommender systems are built to provide recommendations to
travelers. Many programs concentrate on attractions and locations, while
others provide travel packages that include buses, accommodation, and
restaurants.
A personalized sightseeing planning system (PSiS) was created to help tourists
locate a custom tour plan in Porto, Portugal [45]. The proposed hybrid
recommendation approach used strategies CB and CF, fuzzy logic, and combined a
clustering technique with an associative classification algorithm to improve
recommendation performance. SigTur was programmed to provide personalized
tourism recommendations in the Tarragona region [48]. The SigTur has combined
many types of information and recommendation methods to make appropriate
suggestions. The recommender’s information includes demographic data,
specifics describing the travel background, geographic details, etc. The
SigTur uses many recommendation techniques, such as CF and CB techniques,
stereotypes, and artificial intelligence tools.
The SPETA system uses the knowledge of a client’s current position, interests,
and previous location history to determine the facilities that visitors
receive from a professional tour guide [20]. To enhance the interactions of
travelers, this combines social networking and context-awareness techniques.
It provides a customized guide to the tourist and addresses the question of
collapse. A mobile-based recommendation system called SMART MUSEUM makes
recommendations to users on their mobile devices for sites and particles on
those sites [59]. A personalization, description, and information filtering
model based on ontology has been built in this process. Contextual data is
mapped to the ideas described in the ontologies, whether input by users or
captured by mobile device built-in sensors.

Herzog et al. [21] created a smartphone tourist travel recommendation system
named TourRec, which sequences points of interest along nice routes. TourRec’s
core is a modular, multi-tier architecture that facilitates the development
and analysis of new recommendation algorithms, customers, and data sources.
They demonstrate how the TourRec Android device can be used to guide
individuals and groups on tourist trips. TourRec supports the evaluation of
different recommendation algorithms and group recommendation techniques.
PCAHTRS, a personal context-aware hybrid travel recommender system, is
developed by integrating the contextual information of the traveler’s [40].
PCAHTRS is evaluated on datasets of Yelp and TripAdvisor’s real-time large-
scale. The experimental results show that their system has improved
performance over traditional approaches. This will work as a future guide to
help researchers find fruitful solutions to recommendations.

In summary, e-tourism recommendation systems employ different recommendation
techniques according to the degree of complexity and their specifications.
Usually applied to recommend CB, KB, and CF techniques in e-tourism.

##  6 Recommender system in e-government domain

E-government is the use of technical devices, such as computers and the
Internet, to provide public services in a country or region to citizens and
other people. E-government offers opportunities for citizens to have more
specific and efficient access to the government and to provide service to
citizens. The rapid evolution of e-government has caused information overload.
Recommended systems can solve this problem and have been adopted in
applications of e-government. E-government recommendation services are
included G2C (Government to Citizens) and G2B (Government to Business).

###  6.1 G2C service recommendation

A recommendation system known as TPLUFIB-WEB has been introduced to provide
personalized exercises and preventional recommendations for low back pain
patients [18]. With a reduction in travel costs and staff costs, the system
may be used anywhere and at any time. It is very user-friendly, designed for
people with minimal skills, and uses fuzzy linguistic models to increase user
preferences and encourage interactions between users and the system. De Meo et
al. [17]. presented a multi-agent system to support citizens’ access to
personalized and adapted public services. In accordance with the user profile
and the device profile used, the suggested system recognizes and suggests the
most interesting services for the user. The fuzzy-based RS model’s preliminary
results are applied to the Municipality of Quito dataset [47] to recommend
citizens’ payment behavior. The system suggested provides some insights into
using RSs to raise awareness of tax payments among people and thus improve the
income of government institutions.

###  6.2 G2B service recommendation

A recommendation system called BizSeeker has been developed to support the
government in effectively recommending proper business clients to individual
businesses [43]. BizSeeker provides a list of potential business partners for
business users. A method of semantic relevance for the estimation of semantic
similarity was suggested, and a hybrid semantic RS system was created which
combines item-based semantic similarity and CF similarity techniques. A real-
world case study reveals how BizSeeker can solve the problem of sparsity and
improve the accuracy of the recommendation. Business profiles usually present
dynamic tree architectures and user expectations are fuzzy and ambiguous. A
preference tree-based recommendation framework for personalized B2B e-services
has been built and adapted to the business partner’s recommendation system
[68].

Trust or reputation is vital in business partner selection and has a major
influence on the decision of a business user on whether he should do business
with another partner or not. A hybrid trust-enhanced collaborative filtering
RS known as the TeCF approach, which combines enhanced user-based CF and
implicit trust filtering approaches, has been proposed to solve cold start and
data sparsity problems to improve accuracy [63].
In addition to traditional CB and CF, fuzzy strategies that make hybrid
recommendation approaches are being used to improve the performance of
personalized e-government services.

##  7 Recommendation system in e-resources domain

The e-resources services relate to the content that users upload, such as
images, music, files, TV programs, webpages, documents, and videos. Some users
share their resources on the internet so that other users can get resources
that they are interested in. In these sections, we focused on several
recommendation systems applications for resource service recommendations for
both individual and group users.
CoFoSIM, a recommendation method for mobile music, uses multi-criteria
decision-making methods to evaluate partial listening records and implicit
feedback and combine them into composed preferences [36]. The music
recommendation platforms have interesting features that use some implicit
feedback to improve or remove explicit ratings of users.CinemaScreen is a
system of recommendations for movies [60]. One feature of the recommendation
systems for film and music is that it is not easy to get the navigation
history and content from multimedia sources. These resources include features
such as genres, artists, etc. Combination of CF and CB methods in these film
recommendations to solve cold start problems.

Katarya et al. [29] developed a new collaborative recommendation system
focused on films that uses the bio-inspired gray wolf optimizer and the c-mean
fuzzy clustering algorithm to calculate the movie score based on historical
data and user similarity for a specific user. To get the initial clusters, the
gray wolf optimizer algorithm is applied to the well-known Movielens dataset.
FCM is used to classify users by similar scores in the dataset. This
recommender system performed extremely well in terms of precision and
accuracy.
Zheng et al. [74] have implemented a CF-based system of folksonomy
recommenders. In the recommendation process, they manipulated the tag and time
effects. They developed matrixes focused on tag and time relationships instead
of using the score matrix in conventional CF.
Three methods are used to measure the correlations dependent on matrices: tag-
weight, time-weight, and mixed. In online broadcasting, Park et al. [53]
introduced an RS system for real-time online broadcasting named RecTime.
RecTime concurrently takes into account time factors and interests. In
addition, RecTime uses a 4-d tensor factorization that takes into account two
more dimensions of time variables, whereas traditional collaborative filtering
approaches only take into account two dimensions, users and objects. The
system automatically determines both the desired time and the products by
factorizing the 4-d tensor at the same time.

Jalali et al. [25] built an online prediction system called WebPUM and
proposed a new method for classifying navigation patterns of users’ browsing
to predict users’ future intentions. The online prediction system is
efficiently implemented by using web usage mining. The navigation pattern
mining method is based on the new graph partitioning algorithm to model user
navigation patterns. To predict the next user phase, the most common longest
subsequence algorithm is used to distinguish current user operations from
previous ones. Nguyen et al. [51] proposed that the model could navigate more
effectively by combining ontology and semantic knowledge used to evaluate
session results to recommend web pages to users.

Vildjiounaite et al. [66] developed a TV program recommendation system for
family members. This research provides a method for learning a joint model of
a multi-user system from implicit interactions. Program choices are made
collectively and separately by family members. The proposed method adapts to
each family’s behaviors and preserves the family’s privacy, as the common
family model is learned differently for each family.
In summary, the purpose of these e-resource service recommendation systems is
to organize and manage Web service content of this type and to save users from
boring searches. In CF e-resource services, intelligent techniques such as
semantic analysis, bayesian classifier, and decision tree are combined with CB
and CF approaches to implement recommendation systems.

##  8 Recommender system Evaluation Metric

Recommendation system quality is determined by how relevant they are to our
interests and how unusual and interesting they are. We take a look at some of
the most commonly used metrics for evaluating recommendation systems.

###  8.1 Precision

The proportion of relevant things obtained out of all the items retrieved is a
measure of accuracy. Accordingly, if our recommender system selects five items
to suggest to users, and three of them are related, the precision will also be
60%. Precision includes gathering the most relevant items for the user,
presuming that more beneficial products are available than you desire.

$\small Precision=\frac{The\;Number\;of\;recommendation\;items\;that\\\
\;are\;relevant}{Number\;of\;items\;recommended}$ (1)

The proportion of relevant recommended items in the top-k set is called
precision at k.

$Prec\left(R_{k}\right)=\frac{\left|\left\\{r\epsilon R:r\leq
k\right\\}\right|}{k}$ (2)

The number of relevant items from recommendations is r€R, while the total
number of suggested items is k.

###  8.2 Recall

The proportion of relevant things retrieved from all relevant items is a
measure of completeness. If there are five relevant things and the recommender
chooses two, the recall will be 40%. The purpose of the recall is to ensure
that important things do not go missing.

$\small
Recall=\frac{The\;Number\;of\;recommendation\;items\;that\;are\;relevant}{Number\;of\;all\;possible\;relevant\;items}$
(3)

The proportion of relevant things discovered in the top-k recommendations is
known as recall at k.

$Recall\left(R_{k}\right)=\frac{\left|\left\\{r\epsilon R:r\leq
k\right\\}\right|}{R}$ (4)

The number of relevant things from recommendations is r€R, and the number of
relevant items for the provided item is $\left|R\right|$.

###  8.3 Average Precision (AP)

Average Precision (AP) is a ranking precision metric that prioritizes correct
prediction with a high rank. Precision can help to evaluate the model’s
overall performance, but it can’t tell you if the products were appropriately
rated. The AP aids in determining the quality of the recommender model’s
rating of the selected item. If we really need to recommend N items and there
are m relevant items in the entire object space, we can say:

$\displaystyle
AP@N=\frac{1}{m}\;\sum_{k=1}^{N}(P(k)\;if\;k^{th}\;item\;was\;relevant)$ (5)
$\displaystyle=\frac{1}{m}\sum_{k=1}^{N}\;P(k)\cdot rel(k)$

Where P(k) is the precision value at the $k^{th}$ recommendation and rel (k)
is whether the $k^{th}$ recommendation was relevant (rel (k) = 1) or not (rel
(k) = 0).

###  8.4 Mean of Average Precision (MAP)

The mean average precision is MAP. When we have the AP for each user,
calculating the MAP is as simple as averaging it over all users. We’ve only
computed the AP for one user; to determine the system’s overall performance,
we’ll need to compute the AP for each user. We have |U| APs, so we take the
average once more to get a single measure. Because we are taking the average
of precisions computed up to K and computing the mean of average precision@K
for each user to obtain the overall system performance.

$\small MAP@N\\\
=\frac{1}{\left|U\right|}\;\sum_{u=1}^{|}U\left|(AP@N)_{u}=\frac{1}{\left|U\right|}\sum_{u=1}^{|}U\right|\frac{1}{m}\sum_{k=1}^{N}P_{u}(k)\cdot
rel_{u}(k)$ (6)

We are adding up the AP@N for each user normalized by the number of users, and
|U| is the number of users.

###  8.5 F1-Measure (FM)

This measure represents the harmonic mean of recall and precision. The F1
metric considers both precision and recall and creates a compromise between
the two. The F1 measure’s formula is as follows:

$F1-Measure=2\ast\frac{Preccision\ast Reccall}{Preccision+Reccall}$ (7)

###  8.6 Receiver Operating Characteristics(ROC) Curve

The ROC curve can help you determine what threshold will give you the best
results. That’s how it looks graphically. The ROC curve represents the
relationship between properly predicted items (TPR) and wrongly predicted
items (FPR). It gives information if the purpose is to fine-tune the
recommender’s effectiveness by identifying its targets.

  * •

True Positives (TP): Actually Positive and Positive Prediction.

  * •

True Negative (TN): Actually Negative and Negative Prediction.

  * •

False Positive (FP): Actually Negative and Positive Prediction.

  * •

False Negative (FN): Actually Positive and Negative Prediction.

On this curve, two parameters are plotted:

  * •

The term "True Positive Rate (TPR)” is a synonym for "recall," and it is
defined as follows:

$TPR=\frac{TP}{TP+FN}$ (8)

  * •

The following is how the False Positive Rate (FPR) is defined:

$FPR=\frac{FP}{FP+TN}$ (9)

The graph (see Fig. 10) depicts a typical ROC curve.

Figure 10: ROC Curve

TPR vs. FPR at various classification thresholds are plotted on a ROC curve.
When the classification threshold is lowered, more items are classified as
positive, resulting in an increase in both False Positives and True Positives.

###  8.7 Area Under ROC Curve (AUC)

The probability of a random relevant item being ranked higher than a random
irrelevant one is measured by the AUC. The higher the frequency of this
occurring, the higher the AUC value, and hence the better the recommendation
system. In other words, AUC evaluates the entire two-dimensional area beneath
the entire ROC curve from (0,0) to (1,1).

Figure 11: AUC Curve

A typical AUC curve is depicted in Fig. 11.

###  8.8 Mean Absolute Error (MAE)

The mean absolute error is the average of the difference between the value
predicted by the recommender and the actual value given by the user. The MAE
shows how close the predicted score is to the actual score. The MAE of 0
implies that the expected and actual ratings were identical, suggesting that
the model successfully predicted. As a result, a lower MAE is preferable.

$MAE=\frac{\sum_{i=1}^{n}\left|y_{i}-x_{i}\right|}{n}$ (10)

As a result, it’s an arithmetic average of absolute errors, where $y_{i}$ the
prediction and $x_{i}$ is the actual value.

###  8.9 Mean Squared Error (MSE)

The fundamental distinction between Mean Squared Error and Mean Absolute Error
is that instead of negating the negative sign with the absolute error, we
square it. Because MAE penalizes results, even a small difference can make a
big difference. This also implies that the recommender system performed
admirably if the MSE is close to zero.

$MSE=\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\hat{Y}_{i}\right)^{2}$ (11)

Here, $Y_{i}$ is the vector of observed values for the variable to be
predicted, and $\hat{Y}_{i}$ is the predicted values.

###  8.10 Root Mean Square Error (RMSE)

The Root Mean Square Error (RMSE) is a standard method for estimating a
model’s error in predicting quantitative data. RMSE is similar to MAE but
places more emphasis on larger deviations. Although MSE helps to negate the
negative sign, it still scales up errors that really are pointless to compare
to actual rating values due to differing rating scales. To normalize the scale
issue that MSE had, we take the square root of MSE in RMSE. The RMSE will
nearly always outnumber the MAE. To act as a heuristic for training models and
to assess the utility and correctness of trained models. It is formalized as
follows:

$RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(\hat{y}_{i}-{y}_{i}\right)^{2}}{n}}$
(12)

Here, $y_{i}$ is observed values for the variable to be predicted, and
$\hat{y}_{i}$ is the predicted values. And n is the number of observations.

###  8.11 User Coverage

By establishing a threshold that allows only excellent recommendations for
each user in our top n list, adding them, and dividing them by the number of
users in our top n list, we hope to find good recommendations in our top n
list. Our recommender’s coverage is 70% if we have 10,000 things and the model
covers 7,000 of them for various people. If the goal is to provide the user
with as many options as possible, coverage may be a valuable tool for
assessing the recommender model.

$Coverage=\frac{\left|\bigcup_{u\epsilon U}R_{u}\right|}{\left|I\right|}$ (13)

Where U is the set of all system users, and I is the item catalog. $R_{u}$ is
the set of all recommendations given for user u.

###  8.12 Novelty

Novelty contributes to the understanding of the model’s behavior. Users will
be more interested in the same things that they purchased. Therefore, the
novelty may not be effective when proposing items to them at checkout.
However, a place where the customer is still browsing the website and proposes
something wholly new and unique might be valuable, and novelty can assist in
quantifying that. Comparing a user’s suggested products to the population is a
typical way of evaluating novelty. This may be measured in two ways.

$Novelty\left(i\right)=-log_{2}\;\frac{count\;\left(users\;recommended\;\textit{i}\right)}{count\;\left(all\;users\right)}$
(14)

$Novelty\left(i\right)=1-\;\frac{count\;\left(users\;recommended\;\textit{i}\right)}{count\;\left(all\;users\right)}$
(15)

Here i is the item.

###  8.13 Diversity

Similar to novelty, understanding our model’s diversity is helpful depending
on the domain and where we are to offer products. It’s helpful to know how
diverse our model’s recommendations are. As a result of our high diversity,
our users will always have something new and interesting to see and consume.
This is determined by first determining user similarity and then subtracting
it from 1 to determine diversity.

$D=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}\left(1-Similarity\left(c_{i},c_{j}\right)\right)}{n/2\ast\left(n-1\right)}$
(16)

###  8.14 Unexpectedness

Unexpectedness recommends items to users that are different from what they may
expect from the system. It may be used to keep track of marginal progress in
recommendation systems. One method is to compare recommendations to the user’s
previous item interaction history. Another option is to utilize a distance
metric to calculate the cosine similarity between a user’s recommended items
(I) and previous item interactions (H). Higher unexpectedness is indicated by
a lower cosine similarity.

$Unexpectedness\left(I,H\right)=\frac{1}{I}\sum_{i\epsilon I}\sum_{h\epsilon
H}CosineSimilarity\left(i,h\right)$ (17)

###  8.15 Serendipity

Serendipity is a criteria for producing recommendations that are both
attractive and useful. The key advantage of this criteria over novelty and
diversity is the utility of serendipitous recommendations. Unexpectedness
multiplied by relevance, where relevance is 1 if the recommended item is
interacted with and 0 otherwise, is how serendipity is calculated. We only
evaluate the unexpectedness of a recommended item (i) if the user engages with
i.

$Serendipity(i)=Unexpectedness(i)\times Relevance(i)$ (18)

Where Relevance(i)=1 if i is interacted upon, else 0. To get overall
Serendipity, we average over all users (U) and all recommended items (I).

$Serendipity=\frac{1}{count\left(U\right)}\sum_{u\epsilon U}\sum_{i\epsilon
I}\frac{Serendipity(i)}{count(I)}$ (19)

###  8.16 Normalized Discounted Cumulative Gain (NDCG)

The NDCG has three parts. Initial Cumulative Gains (CG).A relevance score is
assigned to each recommendation. The total of all the relevance scores in a
recommendation set is the cumulative gain.

$CG(k)=\sum_{i=1}^{k}G_{i}$ (20)

Here $G_{i}$ is the gain.
Discounted cumulative gain (DCG) penalizes highly relevant items that appear
lower in the search results by lowering the graded relevance value by a
logarithmic proportional to the result position.

$DCG(k)=\sum_{i=1}^{k}\frac{G_{i}}{log_{2}(i+1)}$ (21)

Because the denominator is log(i+1), the items indicated at the top are given
greater weight.

The DCG has one weakness: the score is determined by the number of items
recommended. It’s difficult to compare the DCG score of two recommenders who
each recommend a different quantity of items. IDCG is a solution to this
problem (ideal DCG). IDCG is the DCG score for the most ideal ranking, which
is a top-to-bottom ranking of items based on their relevance up to position k.

$IDCG(k)=\sum_{i=1}^{\left|I(k)\right|}\frac{G_{i}}{log_{2}(i+1)}$ (22)

Where I(k) representsthe ideal list of items up to position k,
$\left|I(k)\right|=k$.

The purpose of NDCG is to normalize the DCG score by IDCG such that its value
is always between 0 and 1, regardless of duration. Normal Discount Cumulative
Gain is defined as the total of gains up to position k in the recommendation
list.

$NDCG(k)=\frac{DCG(k)}{IDCG(k)}$ (23)

##  9 Recommender system challenges and issues

There are some major research challenges and issues related to the recommender
system that are discussed below:

  * •

Cold start: A cold start refers to the case where recommendations for a new
customer and products are difficult to create. Recommendation to a new
customer is difficult because he/she has not yet rated any products, and as
such, there is much less data about customers. Similarly, recommending a new
item is also difficult. Due to a lack of adequate rating information, it is
difficult to find similarities between customers and products. Therefore, the
taste of the new users cannot be predicted and the new items will not be rated
or purchased. This condition decreases the recommendation system’s accuracy.

  * •

Scalability: As there are millions of internet users and products,
recommendation systems face major difficulties in managing enormous amounts of
data. The calculation of recommendations thus increases exponentially, becomes
costly, and sometimes leads to incorrect outcomes. If the dataset size
increases with the number of users and objects, the calculation also increases
linearly. The algorithm works well in a small dataset but cannot produce a
satisfactory outcome for a big data set quantity. Thus, applying the
recommendation method to enormous and dynamic data sets generated by
communication between item users is very hard.

  * •

Sparsity:  Most users have not been involved in rating most products,
resulting in a generally sparse rating matrix and making it difficult to
locate related users. This is an issue that happens when most consumers do not
rate most products, and therefore the matrix of customer products becomes very
sparse. So, there is a decrease in the possibility of having several customers
of the same quality.

  * •

Synonymy:  Similar objects with distinct names or entries are referred to as
synonymy. RS algorithms can not distinguish between closely associated
products like "action films" and "action movies". Extreme use of synonymous
phrases reduces recommendation system efficiency. Using these techniques can
solve the synonymous issues of Singular Value Decomposition (SVD) and Latent
Semantic Indexing.

  * •

Flexibility: Flexibility is the capacity of a recommended system to interact
in a timely and cost-effective way with potential inner or external changes.
Recommendation systems techniques should be flexible for ease of modification
and improvement [50].

  * •

Shilling Attack: A recommendation system is a commercial activity so that
people become biased in their feedback and offer millions of positive reviews
for their products but negative reviews for their competitors’ products and
items. The system, therefore, needs to incorporate some sort of mechanism to
discourage this kind of trend.

  * •

Gray Sheep: The user whose thoughts are not consistent with any group of
people is referred to as the gray sheep. Such applications do not help
collaborative filtering run smoothly. On the other hand, there is a particular
class of users known as Black Sheep whose unconventional conduct makes
recommendations almost impossible. A hybrid system combining CB and CF gray
sheep problems can be solved.

  * •

Privacy issue: A recommendation algorithm needs user demographic input to
generate personalized suggestions of quality; this can lead to data privacy
and safety problems. Therefore, a technique can be designed so that user data
is used decently and carefully to insure that user information is not
accessible to malicious users.

##  10 Comprehensive analysis

Recommender systems are discussed above and summarized in this chapter. The
following significant conclusions can be drawn from the overview of
recommended systems:

  1. 1.

Traditional recommendation methods, such as CB, CF, and KB, continue to play a
dominant position in almost all types of applications, but hybrid
recommendation systems are more common than single recommendation-based
technique schemes to avoid the disadvantages of individual recommendation
methods.

  2. 2.

Some model-based CF methods, such as neural network and fuzzy logic, have been
implemented to address multiple uncertainties in all types of recommender
systems implementation fields.

  3. 3.

Several new recommendation schemes implementation platforms, such as TV and
radio platforms, have only lately appeared.

  4. 4.

The recommendation techniques implemented in a system of recommendations are
limited by the system’s data sources. Numerous sizes of information can be
acquired in the big data age, which is useful for more accurate and
comprehensive modeling of user preferences. It is predicted that more
recommended system apps will be created by using large amounts of information
effectively and efficiently.

  5. 5.

Although many study attempts have been made to address data sparsity issues in
recommender systems, in many implementation fields, this issue is still not
fixed well.

  6. 6.

We found that the number of papers released on the collaborative and content-
based processing techniques in recommendation systems was very large.
Moreover, most of the papers have been published in renowned newspapers. In
journals and conferences, respectively, Springer and Elsevier have the most
published papers.

While research in the development of the recommendation system is increasing
day by day, a major issue is how to apply recommendation methods in real-world
systems and how to solve the problem of large and dynamic data sets. Sometimes
an algorithm works well when tested offline on a small dataset, but it becomes
inefficient when used on large real-world datasets. Moreover, recommendation
systems generate personal recommendations by extracting user data. This means
that this user data must be protected from unauthorized access.The Summary of
the systems developed in recommender system, the techniques used are
summarized in table
LABEL:Summary_of_the_systems_developed_in_recommender_system,_the_techniques_used

##  11 Conclusion

Several algorithms and techniques of optimization have already been proposed
to improve the performance of the RS system over the past few decades. But the
recommendation techniques are associated with several issues that still
require the researcher’s attention. Future work suggests the use of hybrid
techniques with bio-inspired approaches, improving existing methods and
algorithms, machine learning, and profound learning to deliver promising
results.

Table 3: Summary of the systems developed in recommender system, the techniques used System |  Techniques |  Data |  Evaluation |  Application Platform |  Year
---|---|---|---|---|---
OMLCF [4] |  CF, SVM |  Taobao dataset |  Recall, F-measure, Predictive accuracy |  E-commerce |  2021
Fuzzy Recommender System [28] |  Fuzzy logic, sentimentanalysis |  Amazon review dataset |  precision, recall, Diversity, Novelty and Serendipity, nDCG |  E-commerce |  2021
EARS [57] |  Hybrid information fusion, matrix, factorization |  Rating, user social network, user review |  MAP, F-measure |  E-commerce |  2019
CARL [70] |  Context aware, neural network |  Reviews data, user item rating |  MSE |  E-commerce |  2019
MulAtten Rec [39] |  Hybrid, factorization machine, Deep Neural Network |  User review, rating |  RMSE |  E-commerce |  2018
DRS [41] |  Web usage mining, Product based, action based techniques |  Click stream data |  Recall, Precision |  E-commerce |  2015
MRH [64] |  Unified hypergraph |  Music data |  AP, NDCC |  E-commerce |  2011
Consumer electronic products RS [10] |  KB, fuzzy techniques |  Laptop data |  Recall, Precision, F-measure |  E-commerce |  2007
LRRSS [23] |  Adaptive algorithm, Data mining |  User browsing history data |  Recall, precision, coverage |  E-library |  2018
University digital library RS [56] |  Hybrid, fuzzy inguistic modeling |  User interactions data |  Recall, Precision, F-measure |  E-library |  2010
HKBR [65] |  KB, CF, spm algorithm |  Ratings, web logs data |  Recall, Precision |  E-learning |  2017
MoodleREC [16] |  Hybrid Filtering |  Moodle Log database |  Recall, Precision, F1-Measure |  E-learning |  2020
Willow system [61] |  KB |  User interactions data |  SD, SUS score |  E-learning |  2014
PSDLO [7] |  KB, CB |  Web data, personal interests |  Weights comparison |  E-learning |  2009
E-Tourism Package Recommendation [31] |  Hybrid context, squential patter mining |  Flickr dataset |  Precision, MAP, AP, Recall, F1-measure |  E-tourism |  2020
PCA- HTRS [40] |  Context aware techniques |  Yelp and TripAdvisor data |  RMSE, coverage, F-measure |  E-tourism |  2019
TourRec [21] |  Group techniques, Dĳkstra’s algorithm |  Ratings, user’s feedback data |  A/B testing |  E-tourism |  2018
SigTur [48] |  CF, CB, context aware |  Demo- graphic information, ratings |  Spearman correlation |  E-tourism |  2013
SMART MUSEUM [59] |  Ontology, context information filtering |  Context, sensor data |  MAP, Recall, Precision |  E-tourism |  2013
SPETA [20] |  Semantic web, context aware |  Locations data, user preferences |  Recall, Precision |  E-tourism |  2009
E-govern- ment Deep RS [67] |  Deep neural network, random forest algorithm |  My Ningxia dataset |  Recall, F1-Measure |  E-govern- ment |  2020
FBDRS [47] |  Fuzzy logic, knowledge based |  Tax payment, Municipal tax data |  Model Simulation |  E-government |  2019
TBRS [68] |  fuzzy set techniques, tree matching |  Australian business data , MovieLens data |  Recall, Precision, F-measure |  E-government |  2014
TPLUFIBWEB[18] |  CB, CF, Fuzzy linguistic modeling, hybrid |  Multi- media data |  Weight over |  E-government |  2014
TSF [63] |  CF, Semantic filtering, Information fusion |  Movie Lens data, Yahoo! Webscope R4 data |  MAE, Coverage |  E-government |  2012
BizSeeker [43] |  CF, hybrid |  Products taxonomy, description, user rating |  Weight factor, Prediction value |  E-government |  2010
DTRS [73] |  Tensor factorization, polynomial spline approximation |  IRI marketing data, Last.fm data |  RMSE, MAE, PICP |  E-resources |  2021
MovieWatch [72] |  Weighted Slope One-VU, CF, k-means |  MovieLens Dataset |  RMSE |  E-resources |  2019
RecTime [53] |  CF, 4-d tensor factorization |  Watch log data, Electronic Program Guide data |  Timing precision, Recall, F $\alpha$ score |  E-resources |  2017
NMCR [29] |  CF, gray wolf optimizer, FCM |  Movielens dataset |  MAE, Recall, Precision |  E-resources |  2016
WebPage Recommender [51] |  KB, domainontology |  Web log data |  Satisfaction, Precision |  E-resources |  2013
Tag recommender system [74] |  CF |  Ratings, Tag, Time |  Traditional log based model |  E-resources |  2011
WebPUM [25] |  Graph based clustering |  Web log data |  MinFreq, coverage, F1-measure |  E-resources |  2010
CoFoSIM [36] |  CF, ebusage mining |  Clickstream, log data |  Recall, Precision, F-measure |  E-resources |  2010
TV program recommender [66] |  Machine learning, Classifier |  Viewing data |  Recall |  E-resources |  2009
Table 3: Continued From previous page

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Measurements of $W$ and $Z/\gamma^{*}$ cross sections and their ratios in $pp$
collisions at STAR

Jae D. Nam1,* for the STAR collaboration

1 Temple University, Philadelphia, U.S.

*<EMAIL_ADDRESS>
Proceedings for the XXVIII International Workshop
on Deep-Inelastic Scattering and Related Subjects,
Stony Brook University, New York, USA, 12-16 April 2021
10.21468/SciPostPhysProc.?

## Abstract

While the unpolarized parton distribution functions of the valence quarks ($d$
and $u$) are well determined from DIS experiments, the sea quark counterparts,
$\bar{d}$ and $\bar{u}$, are much less constrained, in particular, near the
valence region. Measurements of $W^{+}/W^{-}$ production ratio in $pp$
collider experiments, such as the STAR experiment at RHIC, can probe the
$\bar{d}/\bar{u}$ ratio at a large $Q^{2}$ set by the $W$ mass. These
proceedings will discuss recent results of $W$ and $Z$ cross-section
measurements using the STAR $pp$ collision data at a center-of-mass energy of
$\sqrt{s}=500/510\;\mathrm{GeV}$ collected in 2011, 2012, 2013 and 2017.

##  1 Introduction

Over the past decades, experiments at high-energy particle colliders have
proven successful in broadening our understanding of the nucleon structure and
constraining the parton distribution functions (PDFs) more precisely than
before. Several global analyses, such as CT14 [1], MMHT14 [2], and BS15 [3],
have been conducted in order to extract PDFs from experimental observations.
Nevertheless, there remain open questions that require further experimental
studies. One example is the unpolarized sea quark distribution ratio
$\bar{d}/\bar{u}$, which has been predominantly extracted from Drell-Yan
measurements, such as those at E866 [4] and the SeaQuest [5] experiments.
While measurements from the two experiments are in good agreement in the
region of the partonic momentum fraction $x<0.2$, a discrepancy is observed
for $x>0.25$. This motivates complementary measurements using different
physics processes.

A strong candidate for such a process is the $W$ production in $pp$
collisions, where the $W^{+}/W^{-}$ cross-section ratio is sensitive to the
sea quark distribution ratio. The production and decay channel of a $W^{+}$
($W^{-}$) boson is shown in equation 1.

$u+\bar{d}\rightarrow W^{+}\rightarrow e^{+}+\nu\qquad(d+\bar{u}\rightarrow
W^{-}\rightarrow e^{-}+\bar{\nu})$ (1)

At the leading order, the $W$ cross-section ratio, $\sigma_{W+}/\sigma_{W-}$,
is directly proportional to the sea quark PDFs, as shown in equation 2 [6],
and can be used to probe the sea quark distributions at a large $Q^{2}\sim
M_{W}^{2}$.

$\frac{\sigma_{W+}}{\sigma_{W-}}\sim\frac{\bar{d}(x_{2})u(x_{1})+\bar{d}(x_{1})u(x_{2})}{\bar{u}(x_{2})d(x_{1})+\bar{u}(x_{1})d(x_{2})}$
(2)

Measurements of $W$ production at STAR are also complementary to the LHC $W$
and $Z$ production measurements by providing cross-section measurements with
lower $\sqrt{s}$ and larger $x$.

##  2 Experiment

A detailed description of the STAR detector can be found in [7]. A brief
outline of the components that are most relevant for this analysis is given
below.

The time projection chamber (TPC) [8] is a drift chamber used for particle
tracking, and the barrel electromagnetic calorimeter (BEMC) [9] and endcap
electromagnetic calorimeter (EEMC) [10] are used to measure electrons from
weak boson decays and for triggering. In the mid-rapidity region ($|\eta|\leq
1$) STAR probes the $x$ range of approximately $0.1$ to $0.3$. The EEMC allows
the $W$ cross-section ratio to be measured in the more forward direction,
which extends the $x$ reach to roughly $0.06<x<0.4$.

The first measurements of $W$ and $Z$ cross sections at STAR were based on a
data set taken in 2009, corresponding to an integrated luminosity of
$\mathcal{L}\sim 13.2\;\mathrm{pb^{-1}}$ [11], at a center-of-mass energy
$\sqrt{s}=500\;\mathrm{GeV}$.

Additional data sets were collected in 2011 at $\sqrt{s}=500\;\mathrm{GeV}$,
and 2012 and 2013 at $510\;\mathrm{GeV}$ with a combined luminosity of
$\mathcal{L}\sim 350\;\mathrm{pb^{-1}}$. The results based on these data sets
have been published in [12]. Finally, a preliminary release has been granted
based on the data set collected in 2017 at $\sqrt{s}=510\;\mathrm{GeV}$ with
$\mathcal{L}\sim 350\;\mathrm{pb^{-1}}$.

##  3 Results

$W$ candidates are selected by requiring a large imbalance in the transverse
momentum ($p_{\mathrm{T}}$) deposit in the detector, arising from final-state
neutrinos that escape the STAR detector system. Furthermore, the decay-
electron tracks are tagged by requiring a highly-concentrated energy
deposition in either BEMC or EEMC.

Figure 1:  $E_{\mathrm{T}}$ distributions for electroweak $e^{+}$ (left) and
$e^{-}$ candidates (right).

A comparison of the electroweak (EW) $e^{\pm}$ candidates from the STAR 2017
data set and a Monte-Carlo (MC) simulation (based on Pythia 6.4.28 [13] and
GEANT [14]) is given as a distribution of transverse energy ($E_{\mathrm{T}}$)
in Fig. 1. The MC distribution is a combination of the EW electron signal and
various background contributions; $W\rightarrow e\nu$ MC represents the
$W$-decay electron signal from the MC simulation; the QCD contribution that
survives the selection cuts was estimated from the $E_{\mathrm{T}}$
distribution that fails the $p_{\mathrm{T}}$ imbalance cut, and is denoted in
the figure as data-driven QCD; the second EEMC background is an estimate of
the non-$W$ events that mimic the imbalance in transverse momentum due to
limitations in the STAR detector coverage in the region $\eta<-1$; the
background contributions from $W\rightarrow\tau+\nu$ and $Z\rightarrow ee$
decays are determined from MC simulation. A good agreement is found between
data and the combined MC contributions. Finally, an additional restriction,
$E_{\mathrm{T}}>25\;\mathrm{GeV}$, is applied in order to isolate the EW
$e^{\pm}$ signal from the background. On the other hand, the $Z$-boson
kinematics can be reconstructed by requiring events to have two electrons with
opposite charges.

The selected $W$ and $Z$ candidates are used to extract the $W$ and $Z$
fiducial cross sections as follows:

$\sigma_{W^{\pm},Z}^{\mathrm{fid}}=\frac{N_{W^{\pm},Z}^{O}-N_{W^{\pm},Z}^{B}}{\mathcal{L}\cdot\epsilon_{W^{\pm},Z}},$
(3)

where $N^{O}$ is the number of observed boson candidates, $N^{B}$ is the
number of background events estimated from data and MC, $\mathcal{L}$ is the
integrated luminosity, $\epsilon$ is the detection efficiency, and the
subscript $W^{\pm},Z$ refers to the respective boson candidate.

Figure 2:  STAR $W$ cross-section ratios from the 2011+2012+2013 data set and
the 2017 data set plotted as a function of lepton pseudorapidity.

Shown in Fig. 2 is a comparison of $W$ cross-section ratios from the
2011+2012+2013 data set and the 2017 data set as a function of lepton
pseudorapidity. The vertical bars represent the statistical uncertainties,
while the boxes represent systematic uncertainties. The colored band and
curves correspond to theoretical calculations based on different PDF sets [15,
16] and frameworks [17, 18]. The endcap measurement in the region $\eta_{e}>1$
for the 2017 data set is still under analysis. Only the 2011+2012+2013 result
is shown.

Figure 3:  STAR $W$ cross-section ratios obtained with MC reconstruction
method using the 2011+2012+2013 data set as a function of $W$ rapidity (left).
Differential cross section $d\sigma/dp_{T}^{Z^{0}}$ as a function of $Z^{0}$
transverse momentum $p_{T}^{Z^{0}}$ (right).

The $W$-boson kinematics can also be reconstructed at STAR, as described in
[19]. The neutrino momentum, $\vec{P}_{\nu}$, is determined from the imbalance
in $p_{\mathrm{T}}$ with the help of the MC simulation. The
$R_{W}(\equiv\sigma_{W+}/\sigma_{W-})$ cross-section ratio obtained with the
2011+2012+2013 data set is illustrated in the left panel of Fig. 3 as a
function of reconstructed boson rapidity, $y_{W}$.

In addition, the differential cross section $d\sigma/dp_{\mathrm{T}}^{Z^{0}}$
has been measured with the 2011+2012+2013 data and the preliminary result is
shown in the right panel of Fig. 3. The result is compared to different
TMDPDFs [20, 21].

##  4 Conclusion

STAR has measured the $W^{+}/W^{-}$ cross-section ratio in $pp$ collisions at
$\sqrt{s}=500\;\mathrm{GeV}$ and $510\;\mathrm{GeV}$. These measurements
provide high $Q^{2}$ data that are sensitive to the $\bar{d}/\bar{u}$ ratio in
the kinematic range of about $0.06<x<0.4$, which will help constrain the sea
quark PDFs and complement the E866 and SeaQuest measurements. Furthermore, the
STAR results are complementary to the LHC $W$ and $Z$ production measurements
by providing cross-section measurements at lower $\sqrt{s}$ and larger $x$.
Preliminary results for $W$ and $Z$ production based on the 2011+2012+2013
($\mathcal{L}\sim 350\;\mathrm{pb^{-1}}$) and 2017 ($\mathcal{L}\sim
350\;\mathrm{pb^{-1}}$) data sets are presented, and compared to model
calculations.

## Acknowledgements

We thank the RHIC Operations Group and RCF at BNL. This work is supported by
U.S. DOE Office of Science and DOE-310385.

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