A study on features of social recommender systems

Abstract

Recommender system is an emerging field of research with the advent of World Wide Web and E-commerce. Recently, an increasing usage of social networking websites plausibly has a great impact on diverse facets of our lives in different ways. Initially, researchers used to consider recommender system and social networks as independent topics. With the passage of time, they realized the importance of merging the two to produce enhanced recommendations. The integration of recommender system with social networks produces a new system termed as social recommender system. In this study, we initially describe the concept of recommender system and social recommender system and then investigates different features of social networks that play a major role in generating effective recommendations. Each feature plays an essential role in giving good recommendations and resolving the issues of traditional recommender systems. Lastly, this paper also discusses future work in this area that can aid in enriching the quality of social recommender systems.

Full text

Artificial Intelligence Review
https://doi.org/10.1007/s10462-019-09684-w
A study on features of social recommender systems
Jyoti Shokeen1·Chhavi Rana1
© Springer Nature B.V. 2019
Abstract
Recommender system is an emerging field of research with the advent of World Wide Web
and E-commerce. Recently, an increasing usage of social networking websites plausibly has
a great impact on diverse facets of our lives in different ways. Initially, researchers used to
consider recommender system and social networks as independent topics. With the passage of
time, they realized the importance of merging the two to produce enhanced recommendations.
The integration of recommender system with social networks produces a new system termed
as social recommender system. In this study, we initially describe the concept of recommender
system and social recommender system and then investigates different features of social
networks that play a major role in generating effective recommendations. Each feature plays
an essential role in giving good recommendations and resolving the issues of traditional
recommender systems. Lastly, this paper also discusses future work in this area that can aid
in enriching the quality of social recommender systems.
Keywords Recommender system ·Social recommender system ·Cold-start ·Collaborative
filtering ·Social networks ·Information overload
1 Introduction
The extensive usage of Internet and web services has drastically changed our lives in the last
decade. It has become quite easy to find the information on any item in the world. However,
with the changing information flow over the Internet and more available choices at hand, it has
become difficult to find the relevant and appropriate information. Moreover, while exploring
the search engines, sometimes users are unable to express keywords and feel difficulty in
conveying the requirements. Search engines solve the information overload problem but they
fail to give personalized results. The extensive growth of products on E-commerce sites
and the need to give personalized data is the reason for the development of recommender
systems. Recommender System (RS) is an intelligent system that filters the information and
BJyoti Shokeen
jyotishokeen12@gmail.com
Chhavi Rana
chhavi1jan@yahoo.com
1Department of Computer Science and Engineering, University Institute of Engineering and Technology,
M.D. University, Rohtak, Haryana 124001, India
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recommends useful items and information to users based on user needs. For the past two
decades, there is a rapid rise in implementing RS by different websites. RS is one of the most
prevalent applications of machine learning technology. They assist users in decision-making
when the user does not possess enough expertise to opt an item from an enormous list of
items. It is essential for a good RS to take into account the dynamic requirements of users
(Rana and Jain 2015).
Unfortunately, RSs still experience various problems such as dynamism, cold-start, data
sparsity, privacy and trust (Melville and Sindhwani 2011). Cold-start is a situation when
the requirements are not optimal for RS to work efficiently and give good results. It is the
inability of a system to draw any recommendations for new users and new items as the
system has not yet retrieved adequate information about them. Cold-start problem can be
further categorized into user cold-start and item cold-start problem. It occurs when an item
or a user is new for some service and the system does not have enough similar vectors for
their rating (Abbasi et al. 2014). User cold-start problem arises when a naïve user registers
to the system and for a particular interval of time, the system give recommendations to the
user without relying on his history or previous interactions as no interactions has occurred
yet. With an inadequate elaborated model of user’s preferences, the system fails to build user
profile for the purpose to match relevant items and like-minded users. With the insufficient
information and interactions of user, the system fails to comprehend the interests of user and
consequently give recommendations of poor quality. Due to poor recommendations generated
by the system, users might decide to discontinue the use of system. Similarly, item cold-start
problem arises when the items are new to the systems and no ratings are available for them.
The situation also appears when the items receive very little interactions, even if they have
been available in the item set for months. In certain cases, system may recommend popular
items to cold-start users.
In general, RS is based on large datasets whereas a user rates only a few items from the
vast list of items. Therefore, the user-item rating matrix become highly sparse and leads to
the problem of data sparsity. Data sparsity problem refers to the situation when the system
does not possess adequate user feedback, that is, there are very few item ratings than the
total number of items in the database in order to build an accurate prediction model. This
ultimately reduces the performance of RS. Thus, there is a need of RS that can effectively
deal with these problems and give relevant results.
A remarkable growth of social networks is seen in recent years. Social networking sites
generate an inexhaustible amount of information and their usage can potentially resolve the
issues of RS (Tang et al. 2013;Dakheletal.2018). There are diverse social networking
websites ranging from images and video sharing websites to social tagging and social book-
marking websites. Traditional RS do not use social relations; however, these relations can
be used to produce improved results. Both ratings and social relations can be leveraged to
calculate missing values. When social networks are integrated into RS, it produces a new
system called Social Recommender System (SRS). This system search interesting patterns
by using valuable information from social networking sites. SRS is generating considerable
interest in terms of their features for building effective recommendations (Li et al. 2013;
Dang and Ignat 2017). Despite this interest, to the best of our awareness, researchers have
not yet comprehensively studied all the features of SRS. This paper sheds light on various
features of SRS.
This paper is partitioned into six sections. Section 1gives a concise outline of RS and
social networks. Section 2describes RS and its various techniques. Section 3describes the
concept of SRS. Section 4gives the classification of various features of SRS and explains
each feature with the research work done in the corresponding sphere. Section 5presents the
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comparison of different social recommender systems. Section 6provides the future scope in
this field. We draw our conclusion in the ending section.
2 Recommender system
Today everyone faces the problem of immense set of choices which is called information
overload problem. But selection from the vast set of options in limited time is a difficult task.
Recommender Systems are query-less and collaborative agent that aim to suggest items,
events, links and information to people (Bobadilla et al. 2013). Typically, RS is a set of
algorithms that find data patterns from the available dataset by learning and computing user
preferences. The system then provides the most relevant and useful results on the basis of the
correlation between the interests and needs. The prime aim of RS is to attain user satisfaction
and to establish long-term relationship with users. RS has gained popularity in recent years
due to their assorted applications. A static profile for user is predominant technique in many
existing RSs but it is not sufficient to evaluate the tastes and preferences of users. Dynamics of
RS are important aspects to ascertain accuracy and user satisfaction. In dynamic environment,
user preferences change with time and such changes must be incorporated to increase the
accuracy of RS (Rana and Jain 2015). The accuracy of RS is dominantly influenced by
temporal effects. Thus, incorporation of these factors can greatly improve the accuracy of
RS (Koren 2009). The selection of an approach to develop a recommendation system is also
dependent on the type of data. This data can be content data, interaction data (clicked data)
or the user data.
2.1 Recommender system techniques
In the literature, RS techniques have been classified in different categories like collaborative
filtering, content-based filtering, hybrid, graph, knowledge and demographic based tech-
niques. There also exist other techniques for recommendation such as deep learning-based,
fuzzy-based, utility-based, context aware-based, etc. Some of the techniques have been com-
mercially applied by popular websites like Amazon.com and Netflix.com. Netflix’s prize, for
the best algorithm in movie recommendation, is often the topic of discussion when researchers
talk about RS. Recommendation systems have gained popularity from Amazon.com website
(Linden et al. 2003). We briefly discuss the various techniques of RS as follows:
2.1.1 Collaborative filtering-based
Collaborative filtering (CF) is a technique that finds similar users and uses their interests or
rating patterns to recommend items (Ekstrand et al. 2011). This approach has been success-
fully applied in some specific domains like movies, music and restaurant recommendation.
CF-based RSs are broadly classified into memory-based RS and model-based RS (Su and
Khoshgoftaar 2009). Memory-based methods use user-item rating matrix to search simi-
lar users and similar items. Memory-based methods are further partitioned into item-based
methods and user-based methods. Item-based approaches determine the relationship between
items of the user-item matrix and then compute recommendations based on similarities of
items (Sarwar et al. 2001). User-based approaches identify similar active users from the
user-item matrix, based on their profiles. The similarities between users are employed to
predict ratings. Model-based methods use the training datasets and learn the parameters from
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the datasets to train the model. Once the model is trained on the appropriate database, the
parameters of the model can be used to make predictions. Model-based methods do not
require the investigation of rating matrix. These methods spend most of their time in learn-
ing whereas memory-based methods harness the entire database to make predictions. The
learning of parameters through model makes model-based methods comparatively faster than
memory-based methods.
CF is indeed the most successful technique among other recommendation techniques. It is
used by Amazon1and Netfilix2for recommendations. The goal of CF is to recommend a user
an item which has been liked by people with similar tastes. In these systems, the profiles of
users are expressed in context of their priorities for the items. Some mathematical functions
are then used to calculate the similarity between the items. For illustration, suppose there are
seven items A, B, C, D, E, F and G and there are three users X, Y and Z in the system. Let
user X likes items A, C, E and G; user Y likes items A, B, E and G; and user Z likes items
D and F. In such a case, a CF-based system would recommend item C to user Y as user X
and user Y have similar preferences (namely A, E and G). On the other hand, the system
does not recommend any item to user Z because the preferences of user Z do not match with
any other user in the system. Such a user for whom the RS fails to make any meaningful
recommendations is called grey sheep. As there is massive number of items in the system,
the user preferences for the items are generally very sparse. Due to sparsity in preferences of
items, CF-based systems have a huge number of grey sheep users. In addition, the accuracy
of CF-based systems is also dependent on the total number of rated items.
The advantage of employing CF is to recommend more serendipitous and diverse recom-
mendations. It has the ability to recommend “out of box”. It may suggest rock music to a
listener who is interested in hip hop genre. When the user space is very large, CF techniques
perform best. Also, there is no item cold start problem with this technique. Another advantage
is that it grasps the changing user preferences over time. Nevertheless, CF has several disad-
vantages. As tastes of people are different in different arena, therefore this approach may fail
in large diverse domains. Research paper recommendation is an example of a domain where
CF approach has not a very good performance due to data sparsity. There are few users in this
domain but millions of papers due to which there are very rare chances where two users give
common ratings to papers. There are many research papers which are not rated by any user.
Consequently, CF-based RSs do not recommend such papers. CF requires item metadata
due to which their extension to cross domains turns out to be difficult. Other shortcomings
of this technique are user cold-start problem and the synonymy issue (Isinkaye et al. 2015)
where two items are similar but they are termed differently. It usually becomes difficult for
CF-based systems to find the correlation between the items. Latent Semantic Indexing and
constructing a thesaurus are some of the methods to solve this problem. However, adding
of synonyms words by these methods may have different meanings than what is expected
which may produce poor quality recommendations. Lastly, CF-based systems are vulnerable
to profile injection attacks or manipulation where an anonymous user can give false ratings
intentionally. Such an attack is called shilling attack. In this situation, users give positive
ratings to their own products and negative ratings to the products of their competitors.
1https://www.amazon.com.
2https://www.netflix.com.
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2.1.2 Content-based filtering
A content-based filtering (CBF) RS recommends items that are similar to the historical
preferences of users (e.g. in a movie RS, if a person rated some thriller movies earlier
then next time this RS will presumably recommend recent thriller movies to that person).
Such systems are user-independent, transparent and capable of recommending new items.
User feedback plays an essential role in these systems. As a result, CBF systems give more
personalized recommendations.
CBF techniques are useful in text-intensive areas where recommendations similar to the
keywords are significant (Perugini et al. 2004). For instance, given online open courses, and
knowing that a user opted “Coursera” and “edX”, the system could easily infer the propensity
of user towards online educational courses and could therefore recommend “Udemy”. CBF
techniques compare representations of item’s contents to representations of contents that
fascinates the user. According to a survey, more than 50% of research paper RSs used CBF
technique (Beel et al. 2016). One major advantage of CBF systems over CF systems is that
the former is capable of recommending new items by looking at item contents whereas the
latter need ratings for new items before recommending them. However, a user rates only
few items from the vast set of ratings. Such limited content information is insufficient for
categorizing information and generalizing user’s interest. Therefore, such systems tend to
generate similar recommendations and experience the problem of overspecialization. For
instance, when a customer opt for a mobile phone, he may give more importance to its
cost than its color. In contrast to a CF system, a CBF system trained with features of rock
music would not recommend hip hop music. In addition, these systems may not give reliable
recommendations to a naïve user due to insufficient or no ratings. One solution to this problem
is to create regression and predict user preferences using the demographic data.
2.1.3 Hybrid-based
Hybrid-based techniques combine two or more than two techniques to give improved recom-
mendations. Generally, this technique combines the best features of CF and CBF techniques
to avoid the cold-start problem. There are basically three ways to integrate these techniques.
The first approach is implementing CBF features into CF technique. The second approach is
implementing CF features into CBF technique and the third approach is to execute both these
approaches autonomously and then use their predictions to generate recommendations. Other
techniques can also be combined to give users more valuable recommendations. The central
idea behind combining different approaches is to restrain the shortcoming of a technique into
a hybrid model and increase the overall performance of system. For instance, a knowledge-
based technique and a CF technique might be coupled so that doamin knowledge could be
utilized to mitigate the cold-start problem. This assist RS to generate recommendations to
new users. On the other hand, CF-based technique is capable in finding similar users which
a knowledge engineer would not be able to anticipate.
An important question during hybridization is to determine which techniques should be
combined in which domain and at what point to make the system successful with good
performance and accuracy. Burke (2002) has given a broad survey of hybrid RSs. They have
summarized the actual and other possible hybrid recommendation techniques that combine
different kinds of recommendation techniques. For example, cascading two different CBF
techniquesis gives a hybrid technique. Some hybrid techniques combine the approaches
which belong to the same set of technique but are implemented differently. Recently, deep
learning techniques are excessively integrated with aforementioned techniques to generate
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more robust recommendations. Liu et al. (2018) integrate CF with deep neural network to
learn item and user features.
2.1.4 Graph-based
A graph-based RS use a graph model where users and items represent the nodes, and edges
express the interactions between user-user or user-item. A graph-based recommendation
technique mainly consists of constructing a graph that indicates the data and giving recom-
mendations on the basis of graph analysis. With a graph-based technique, it becomes easy
to find users with similar tastes. The technique can discover similar items and also the items
that have been rated or bought by the users. The benefit of building graph-based RS is that
once the graph is constructed, it is easy to add nodes by inserting the nodes and their connec-
tions to other nodes. But it is uneasy to unlock new items in graphs. Some astute techniques
are required to uncover new items. Scientific paper recommendation and searching items
in digital library are the areas where graph recommendations are useful. Depending on the
way the graph is modeled, it becomes simple to discover closely related items and users.
Nevertheless, the ordering of results can end up recommending identical items constantly.
An early work in the sphere of graph-based RS is in the context of book recommendation
that combines CF and CBF approaches (Huang et al. 2002).Weietal.(2013) perform research
in discriminating social ties by incorporating three graph-based algorithms, namely HITS,
PageRank and heat diffusion. They leverage these algorithms to distinguish and propagate
the influence of different friends in the social network. Pham et al. (2015) use the graph-based
approach to build a heterogeneous graph model for event-based social networks. The graph
models the interactions between different entities like users, groups, events and tags. The
model is designed to recommend events to users, recommend groups to users and recommend
tags to groups.
2.1.5 Knowledge-based
A knowledge-based RS is based on user-specified interests rather than the former taste of
users. It distinguishes itself from other techniques by implementing a a knowledge-based
technique that employs knowledge about the user and the products. The system combines
user-specified requirements and item attributes with the domain knowledge (Aggarwal 2016).
Such systems do not make use of ratings for generating recommendations. Instead, they
use domain knowledge-based similarity metrics to construct the knowledge base. However,
the construction and then updation of the knowledge base is a difficult task as it requires
enough expertise and domain knowledge. Knowledge-based systems are useful in domains
where rating-based systems (CF and CBF) do not work. Some items like luxurious articles,
automobiles and property are not often purchased. In such cases, knowledge-based techniques
are proved to be effective. They use features of items and then generate user profile to make
recommendations. There is no cold-start and grey sheep problem in these recommender
systems but they tend to generate static recommendations according to what is contained
in the user database. Knowledge-based RSs are sensitive to changing preferences of users.
The changing user preferences are essential for generating good recommendations. This
make knowledge-based recommendation techniques valuable than other recommendation
techniques. Based on the interface type, Aggarwal (2016) classifies knowledge-based RS
into constraint-based RS and case-based RS. However, the need of knowledge engineering
skills is the main weakness of these systems (Burke 2007). Recently, Tarus et al. (2018)have
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given a detailed review of ontology-based RS for knowledge based recommendations. They
have highlighted how ontology is informative for knowledge representation and consequently
in improving the robustness of recommendations.
2.1.6 Demographic-based
A demographic RS categorizes users on the basis of their demographic attributes like name,
gender, age, country, qualifications and occupation (Al-Shamri 2016). The system uses the
demographic attributes to train the classifiers that can map demographic information to rat-
ings. In contrast to other techniques, the advantage of demographic-based technique is that
it may not need user’s rating history. This makes this approach simple, easy and fast to
give recommendations using few observations. Zhao et al. (2016) extracted user profile as
demographic data from social media for product recommendations. However, it is difficult to
retrieve demographic data due to privacy issues. Demographic RSs are generally of stereo-
typical nature as they are based on the idea that users belonging to a common demographic
group have common interests. Typically, demographic-based techniques do not give the best
recommendations when executed alone. The accuracy of these systems increases when they
are combined with other techniques like knowledge-based technique. Another limitation of
these systems is the lack of availability of demographic data on online networks as users feel
reluctant to share their personal details for the fear of maltreatment. Wang et al. (2012)inte-
grated demographic RS with three machine learning algorithms to examine if demographic
information alone is sufficient for good recommendations for visitors. Their experiments
demonstrated that demographic recommendation techniques must be combined with other
techniques to generate valuable predictions.
Each of the above recommendation system techniques has both strengths and limitations.
Table 1compares these techniques on the basis of advantages and disadvantages.
Table 1 Comparison of different techniques of recommender systems
S. No. Techniques Advantages Disadvantages
1. Collaborative filtering Serendipitous and diverse
recommendations, no item
cold-start problem, no domain
knowledge required
User cold-start problem, data
sparsity, grey sheep problem,
synonymy, shilling attack,
scalability, quality based on
ratings
2. Content filtering User-independent, transparent,
new recommendations, no item
cold-start
Overspecialization, user feedback
is essential, user cold-start
problem
3. Hybrid Makes system robust, improve
performance
Knowledge of combining
techniques for a particular
domain
4. Graph Easy to find similar users and
similar products
Ranking of results, modelling of
graph
5. Knowledge No cold-start problem, no need of
ratings
Need of knowledge acquisition,
static recommendations
6. Demographic No need of ratings, improvement
in recommendations with time
Privacy issue, low accuracy, lack
of availability of demographic
data, new user problem
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3 Social recommender system
Social Recommender System is the integration of social media and RS. Both social networks
and RSs share their mutual benefits. The immense expansion of social networks has generated
opportunities for researchers to analyze social networks and use their findings in RSs. Social
network is a major research area from the past many years but traditional RSs do not involve
social influence (He and Chu 2010). Social networks have emerged as an important investi-
gation field for content sharing and communication. Millions of social active users spend a
percentage of time on these social networking websites daily (Bellman et al. 1999). These
social entities create accounts, connect with friends, join some communities, post comments,
tag the resources and give ratings. The social networking users create and publish their per-
sonal information such as hobbies, education, interests and gender from which voluminous
knowledgeable data can be generated. Users sharing similar tastes and preferences in a social
network tend to form groups.
Social networks provide a variety of sources of information which was not been possible
before few years. Social media generates a huge amount of information in the form of
relationship, comments, ratings and tags. This readily available social data can be harnessed
in RSs to improve the prediction performance. On the other hand, RS play a major role
in enhancing recommendations in social media and addressing exciting issues like friend
recommendationa and social engagement. Consequently, RSs are crucial for the successful
application of social media. SRS lowers the information overload problem by providing the
most robust results to users. It employs the concept of social influence to ascertain user
preferences. The recommendations in SRS are affected by the ratings of friends rather than
the ratings of anonymous users.
4 Classification of social recommender systems
In this area, the work is being spread along various dimensions where a number of questions
are being raised in distinct classifications. Majority of the traditional RSs exploit CF approach
to find similar users and then assign ratings on the basis of similarity. The similarity between
the users can be measured only if they give common ratings to some items. However, CF-based
approaches often suffer from the problem of data sparsity due to limited number of ratings
in user-item matrix. Social recommenders produce more appropriate recommendations by
means of social relations and communities. As a result, SRS can easily handle problems like
cold-start problem, data sparsity and trust.
In this study, we propose the classification based on a number of parameters that caters
to the features of SRS. The identified features of these systems are context, tag, trust, group,
cross-social media data, temporal dynamics, heterogeneous social connections and semantic
filtering. Although these features have been applied autonomously in different SRSs in the
literature, but none of them has classified all these parameters. When these features are
harnessed to build SRSs, computational complexities could certainly increase but the resultant
recommendations could be relevant and remarkable. This section presents the state-of-the-art
of these 8 features of SRS.
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4.1 Context
Many existing recommendation methods suggest items to users without using the contextual
information. However, such recommendations may not prove to be useful and relevant every
time. Contextual information is an important factor in giving appropriate recommendations
according to the time, situation, location and mood of user. A good RS is the one that
gives relevant information to user based on user’s preferences at the correct time and on
right place. The concept of context has been analyzed in different disciplines and even in
sub-categories of the same discipline due to which multiple definitions of context exist.
According to Dey et al. (1998), “a user’s context is any information about the user and the
environment that can be used to enhance the user experience”. In other words, context is a
social, physical, mental and emotional state of a user. The prime aim of context-aware RS is
to give appropriate recommendations based on the user’s current context. A context-aware
RS attempts to incorporate extra facts, other than the user and item information to predict
users’ preferences on unseen items (Pagano et al. 2016). However, ascertaining how and
when to include contextual information in these systems is challenging (Adomavicius and
Tuzhilin 2011).
A social network based context-aware RS known as SoCo is presented by Liu and Aberer
(2013). SoCo uses random decision trees to integrate diverse forms of contextual information
and splits user-item rating matrix to group items and users based on the context. They com-
bine the contextual information with social information into a matrix factorization model to
give quality recommendations. They introduce a term called social regularization to address
heterogeneity in tastes of social friends. Ma et al. (2011) coined the term social regularization
to constrain the diversity in tastes of user and his/her friends. Social regularization defines that
users who are socially connected are likely to share similar preferences. This term imposes
social constraints on RSs. Macedo et al. (2015) expose new possibilities to exploit various
contextual signals from event-based social networks even in the absence of user interactions.
They propose a context-aware approach by exploiting different contextual signals, namely,
location signals based on user’s topographical choices; temporal signals based on user’s time
priority, and social signals derived from the user’s group membership.
Location is a vital factor in assessing a user’s context. Location history plays an important
role in learning user’s preferences and behavior. Location-based social networks contain
rich knowledge in the form of temporal, spatial and social data. Location is a dimension in
location-based social networks that acts as a bridge in building new connections between
users and locations, between users, and between locations (Bao et al. 2015). A number of
location-based social networking sites, such as Yelp and Foursquare have grown up where
users share their reviews and likes about the locations they visit. These social networks aim
to recommend places of Point-of-Interest (POI) to users on the basis of their preferences
and locations. POI recommendations are location-based, personalized and context-aware.
Many studies leverage geographical data to generate POI recommendations (Ye et al. 2011;
Liu et al. 2013a; Liu and Xiong 2013). Liu et al. (2013a) follow an integrated approach of
combining the effects of different factors to learn geographical preferences. They propose a
probabibilistic factor analysis POI recommendation framework to automate the user check-in
behavior. The factors are user mobility behavior, geographical influence, user preferences
and implicit user feedback. On the other hand, Liao et al. (2018) use a fine granularity and
tensor factorization approach for POI recommendation in location-based social networks.
They extract fine-grained topics from the user comments and use Latent Drichlet Allocation
(LDA) model to generate POI-topics distribution. Then, theydivide user check-in information
into multiple slices of time and integrate it with POI-topics to construct time-based user-topic
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distribution. The user-topic-time tensor is constructed and then decomposed to build a dense
tensor. Capdevila et al. (2016) use CF anf CBF techniques to propose a hybrid RS called
GeoSRS that recommends locations to users. While geographical venues and short reviews
are used as the source of information, they use state-of-the-art techniques for sentiment
analysis and text mining on textual reviews.
Recently, Sassi et al. (2017) present a comprehensive outline of context-aware system
that identify contextual signals in mobile environment. They emphasize that combination of
too many contextual signals can reduce the quality of recommendations. Lastly, Colombo-
Mendoza et al. (2018) propose an ontology and knowledge-based context-aware SRS in the
eating domain. The model explicitly gathers the information and uses ontology to model
and construct this information for personalized recommendation. This model consists of
seven modules, namely, a knowledge repository, domain ontology, a knowledge integrator,
a knowledge-based recommender, a context-aware system, a user interface and a LDA-topic
discoverer.
4.2 Trust
A trustful RS is required to distinguish authentic and malicious users (Manasa et al. 2017).
Trust can be stated as an umbrella term that covers a varied sort of meanings. Trust could
be defined as the level of confidence for the ratings given by the user. In a social network,
trust assesses the willingness of a user to behave in an expected way (Abbasi et al. 2014).
In context of RS, it expresses the accuracy of recommendations that the system generates.
A trust relationship is established in a social network on the basis of social ties between
people (Carrasco 2012). The trusted relationships among users in a social network forms
a trust network. When social links are annotated with trust data, then this produces a new
RS called trust-aware SRS. It is important to know the level of trust in trusted relations for
making decisions. This is the reason that users give preference to those RSs that rely on trust
relationships rather than similarity measures.
A trust-aware SRS can solve the cold-start, data sparsity, rating integrity and scalability
problems because acquainted users are more trustworthy than unknown users (Gao et al.
2015;TianandLiang2017). There are different facets of trust representing diverse types
of relationships among users (Tang et al. 2012). Recently, Shokeen and Rana (2018b) focus
on the features of trust-aware SRSs. They discuss the following properties of trust-aware
SRSs: non-transitivity, composability, personalization, asymmetry, domain dependency and
time dependency. They also discuss trust metrics and the factors affecting recommendations.
They address how to infer trust and dynamically update it in social networks.
Li et al. (2013) combine the ideas of CF and CBF techniques to develop a new SRS for
recommending products in e-commerce websites. This system uses preference similarity,
social relation analysis, recommendation trust and personalized recommendations. On the
other hand, Shen et al. (2016) used CF algorithm to propose a system for e-commerce
websites. They have improved CF algorithm by combining reputation-based trust, preference
similarity and social relations between users. Yang et al. (2017) have tried to enhance the
performance of CF approach by proposing TrustMF as a social collaborative filtering method.
TrustMF is a matrix factorization method that integrates rating data and trust data. They have
also proposed a truster model and a trustee model to explicitly explain how users must
influence and follow the ratings or opinions of others.
Further, privacy is also an issue in SRSs as these systems exploit user’s personal details. To
address this issue, Dang and Ignat (2017) have recently proposed dTrust as a rating prediction
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approach that does not need user’s personal information. However, this approach uses the
topology of trust-user-item network. It uses deep feed-forward neural network to combine
user relations with user-item ratings for rating prediction.
Tian and Liang (2017) leverage trust relationships of social networks to present an
improved recommendation algorithm. They have employed both direct trust and indirect
trust relationships for improved recommendations. Cui et al. (2017) developed a trust-based
video recommendation algorithm to recommend videos in online social networks. For this,
they developed a user discovery model and a video discovery model and then combined
these models. They segmented the users into direct and indirect influential users to find the
renowned users. In user discovery model, the trust between target user and influential users
is computed based on user similarity, interaction and friendship. The video discovery model
calculates the video trust using video activities and video ratings.
4.3 Tag
Social networking sites allow users to upload information or resources in the form of images,
documents, videos, websites, and check-ins. These resources are assigned a label termed as
tag and the practice of assigning tags is termed as tagging. Tags are the words customised by
users to express their opinion, location, mood, time, etc. They act as a connection between a
resource and a user and their frequent usage demonstrate the interest of a user towards that
resource (Zheng and Li 2011). Social tagging is an important parameter in exposing user’s
preferences resulting in augmenting the performance of SRS (Milicevic et al. 2010). The
purpose of tagging is to share and discover the resources. With the boom in social networking
websites, social tags have started gaining the popularity. The preference to resources for a
particular person changes with time and the more recent bookmarked resources or tags
express the current interest of a user. So, recent tags are given more preference for better
recommendations.
Basically, there are two viewpoints to recommend tags: user-centric methods and
document-centric methods. User-centric methods aim to find similar users or related groups
and use their past tagging behavior to recommend tags whereas document-centric methods
categorize documents into different categories by performing document-level analysis. User-
centric methods are less effective in recommending tags because of two main reasons. First,
very few people carry out tagging extensively and second reason is the less reusability of
tags when there is a constant growth in terminology of tags (Farooq et al. 2007). Chirita
et al. (2007) used document-centric approach to propose P-TAG method for automatic gen-
eration of personalized tags semantically. In their approach, latent semantic analysis and
cosine similarity are used to compare a web page with a desktop document. On the other
side, Song et al. (2011) use a different perspective to mechanize the procedure of generating
tag recommendations to users on the arrival of a fresh resource. They use machine learning
method to propose two document-centric approaches where the first graph-based approach
uses bipartite graphs to represent tagged data with the intention to find document topics.
Another approach is prototype-based that discovers the most representative documents from
the data collected and uses Gaussian process classifier to recommend multiple tags concur-
rently. Both these approaches use a ranking method to rank the tags on the basis of existing
popularity of tags.
Social tagging sites allow users to produce system-based content through tagging. One
can share photos on Flickr and Instagram, share papers citations on CiteULike and Mendeley,
set life goals on LifeTick and 43Things or publicize social bookmarks on Delicious, Digg
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and LinkedIn. All these systems or websites are similar in the sense that users can upload
resources after logging in and then tag them. The set of tags or vocabularies of a particular
user are termed as personomy and the set of all personomies or all the assignments of all users
is called folksonomy. Pan et al. (2012) have proposed a method for expansion of tag neighbors
by combining k-nearest neighbor algorithm and clustering approach. It is assumed that the
tags that co-occurred frequently or having higher similarity weights are the neighbors of each
other. This method ranks the tags in the descending order according to the similarity values.
The expansion of tag neighbors ultimately improves the accuracy of social recommendations.
Huang et al. (2014) highlight the importance of tags that are recent, recurrent and existing
from a longer period for social resource sharing websites. Based on the user’s social tagging
information, they propose a hybrid method that uses CF to discover similar users and CBF to
find out similar items to enhance the quality of recommendations. Zhou et al. (2015) focus
on the data sparsity issue of user-based CF methods as these methods rely solely on ratings.
With an attempt to resolve this issue, they integrate the social relations and user-generated
tags into the user-based CF technique to give better recommendations. However, they treat
the same types of tags as equal although they imply different meanings in reality.
Different people have different interpretations for a tag. In addition, different tags can
have similar meanings. Arnaboldi et al. (2016) have proposed PLIERS (Popularity-based
Item Recommender System) as a tag based RS. This RS is based on folksonomies and
recommends those items or tags which match the popular items already owned by the user.
Generally, users choose their own vocabulary to tag items. This is the reason that social
tags contain uncontrolled vocabularies and are generally sparse, ambiguous and redundant
in nature. To solve the problem of uncontrolled vocabularies in tagging, Xu et al. (2017)
have followed a deep neural network approach to propose a deep-semantic similarity-based
personalized recommendation (DSPR) framework. This model uses tag-based user and item
profiles as inputs. The inputs are mapped into an abstract deep feature space to maximize the
similarity between users and target items. They have also proposed a hybrid deep learning
model with negative sampling to improve the efficiency of model training and approximate
the noise. Recently, Shokeen (2018) leverages tags, social information and contextual details
to inspect the role of social networks in project recommendation.
4.4 Group
Most of the RSs give item recommendations to users individually. But there are some circum-
stances and domains where users work in a group to execute some activities like performing
research in a specific area, reading a novel, going for a picnic or attending a conference. More-
over, there are contexts such as satellite systems where recommending individual schedules
to individual users are difficult. RS that generate recommendations for a group of users is
termed as group RS (Masthoff 2011). A rapid growth of developing group RSs has been
seen in recent years. In social networks, communities are the group of users having similar
preferences.
Quijano-Sanchez et al. (2013) uses social trust factors and group personality composition
to develop a group recommendation method. This method computes the level of trust by
aggregating the social factors such as total mutual friends, social distance, strength, duration
and intimacy of relationships, status and common interests. These social factors represent
the characteristics of social network groups. Christensen et al. (2016) focus on exercising
social influence for improving the accuracy of RS in tourism domain. They present a hybrid
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approach that integrates CF, CBF and demographic filtering for group recommendation and
uses social relationships along with individual and group preferences.
Earlier, many group RSs aim at recommending items to a cluster of users. However, Guo
et al. (2016) follow a different perspective for recommending groups to individual users.
They study the factors responsible for driving users to join groups. They take advantage of
user-based CF and trust-based CF (Massa and Avesani 2007) methods and combine these
techniques to propose a group recommendation model. User-based CF method is used to
estimate tastes of similar neighbors whereas trust-aware CF method is used to find tastes of
user’s trusted neighbors. They observe that users with both high active degree and low active
degree are more interested in joining the groups. They also observe that fresh users like to
join groups of their interests. But when these users become more communal and have many
followees, they prefer to enroll in the groups joined by their followees. Most of the group RSs
use a consensus function for aggregating preferences of individuals. Furthermore, Hong et al.
(2017) follow a different viewpoint that is based on a weighted strategy. The weight strategy
assumes that the type and strength of relationships decide the importance of a user. They
propose a method called GRSAT to enhance the performance of group recommendation. This
method exploits two social factors, viz., social affinity and trustworthiness. User influence
in a group ascertains the social affinity whereas loyalty of a user in the group ascertains
the trustworthiness. GRSAT generates a weighted consensus function by combining the
above two social factors. But the performance of this method reduces when the group size
increases. Gottapu and Monangi (2017) use location-based social networking information
(such as check-in data) to design an algorithm that gives POI recommendations to mobile
social groups. They discover the locations used for group events and then identify the users
who visit a POI as a group. Based on the identified groups, they generate their corresponding
POI signature that describes various properties of the groups like total users, total relations,
closeness, etc. For every newgroup seeking recommendations for a particular POI, the method
calculates the signature for that POI and uses K-nearest neighbor algorithm to generate
recommendations.
4.5 Cross social media
It is usually seen that people create their accounts on multiple social networks. For instance, a
user having an account on website MovieLens can also have an account on website Epinion.
The intuition behind using data from multiple domains is that items of one domain could
be correlated to items of other domains. Suppose a user has an account on MovieLens for a
long time but that user is new to Epinion, then the information about user from Movielens
can be used an additional knowledge by Epinion. Recognizing individuals on multiple social
networks improves the accuracy of user profiles and ultimately enhance the quality of rec-
ommendations (Zafarani and Liu 2013). In literature, a number of methods are proposed for
user identification in different domains. Social networks contain useful social interactions
in context of different items. Most of the social interactions are easily accessible from pop-
ular social networking websites such as Twitter, Tumblr and Facebook. In previous works,
researchers have emphasized that social interactions are efficient sources for improving rec-
ommendations (Zhao et al. 2013). A method to identify match between items and users in
different domains is given in Li and Lin (2014). This method uses transfer learning method
to transfer information from one domain to another domain to enhance rating predictions.
Another approach that uses transfer learning to improve social recommendations is given
by Jiang et al. (2015). They develop a Hybrid Random Walk (HRW) approach to transfer
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knowledge from one domain to another domain. The knowledge from auxiliary domains is
employed to determine tie strength which is then used for user behavior prediction. This
approach gives promising results in alleviating cold-start and data sparsity issues. Farseev
et al. (2015) examine the impact of multi-source data on recommendation performance.
For this, they integrated data from Twitter, Instagram, and Foursquare for same users. They
exploit the multi-source and multi-modal data to develop a cross-domain collaborative RS
for venue recommendation.
Nowadays, many users have multiple accounts on social networking sites and information
domain sites. Such users serve as a bridge in propagating user-item interactions and ratings
across different domains. Wang et al. (2017) consider these users as silk route to recommend
items from information domain to social domain. They propose neural collaborative social
ranking (NCSR) solution that integrates user-item interaction from information domains and
the user relations from social domains to give cross-domain social recommendation. They
devise pairwise pooling operation to model interaction between user, items and their features
from information domain. They use deep neural networks to train the low-level interaction
features to get higher order interactions. Khan et al. (2017) conducts a systematic review
of cross domain RSs in which they classify systems on the basis of their extensively used
building-block definitions. Ma et al. (2018) recently proposed a transitive trust-aware cross-
domain recommendation (TT-CDR) method that utilizes context-dependence, ratings and
transitive dependence to improve social recommendations.
4.6 Temporal dynamics
Customer preferences and items popularity are extensively nonstationary in real world sce-
nario. The accuracy of a RS is based dominantly on temporal effects (Koren 2009). For
instance, a woman who is interested in buying baby toys may shift her preferences to buying
books later. Also, relationships in social networks change with time. A user is free to form
new relations with other users and even end existing relations with some users. The updat-
ing of social connections influences the ratings of items. De Pessemier et al. (2010)have
experimentally proved that old data have negative impact on the precision of RS.
Pham et al. (2015) highlight the impact of temporal dynamics on user behavior in event-
based social networks. They introduce a graph-based model for recommending multiple
types of information in these networks. Gao et al. (2013) propose a location recommenda-
tion framework taking into account temporal effects. They found that non-uniformness and
consecutiveness are the two temporal properties which are strongly correlated with users’
check-in preferences and the corresponding check-in time. Non-uniformness is the property
that defines that a user has different check-in preferences for different hours in a day whereas
consecutiveness defines that a user tends to exhibit similar check-in preferences in consec-
utive hours. They leverage these temporal properties in location-based social networks for
location recommendation.
Kefalas et al. (2018) focus on including time dimension for location and friend recommen-
dations and propose a hybrid tripartite graph (i.e., users, sessions, locations). Their method
is a variation of RWR algorithm known as Random-Walk with Restart on Heterogeneous
Spatio-Temporal graph (RST-HST). The proposed graph consists of 7 distinct unipartite and
bipartite graphs. User-time, location-time and user-location are the three bipartite graphs used
in this method. To further enrich the information, edges between the nodes of same set are
used to form the unipartite graphs like user-user, location-location and session-session). They
create artificial nodes called session nodes to include time dimension in tripartite graph. Ses-
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sion nodes associate the co-location of the users. They follow a star-schema graph to directly
connect the users with both sessions and locations. On the other hand, Gurini et al. (2018)
follow a different approach that is based on semantic attributes identification and different
fromPhametal.(2015) user similarity approach. They propose a RS that exploits user’s
attributes: sentiment, objectivity and volume which are extracted from semantics of tweets.
The exploited knowledge supplies recommendations to follow appropriate people. They clar-
ified that people with similar interests may express different feelings or opinions. Finally, they
proved that temporal changes in user’s attributes improve people-to-people recommendations.
Zhang et al. (2017b) use time decay function and social relations for friend recommendation
in Weibo. They use LDA model to extract user-topic features and Jensen-Shannon diver-
gence to estimate interest similarity. They propose two recommendation models in which
one is based on social relations (SR-FR) and another on time sequenced topics (TLDA-FR).
Recently, Shokeen and Rana (2018a) highlighted the importance of combining temporal data
with other parameters to improve the performance of SRS.
4.7 Heterogeneous social connections
As social networks are heterogeneous in nature, there exist multiple types of social con-
nections between users. These social connections are a mixture of relations ranging from
positive to negative, weighted to unweighted, important to ordinary and directed to undi-
rected relations. Different types of relations between people influence them differently. For
instance, a researcher’s topic can be influenced by his guide but may not influence his daily
life. Free, fast and easily growing connections on social networks has created superfluous
number of social connections in the form of friends. It is not necessary that all the relations
are equally important. Close friends build important connections, ordinary friends develop
less important relations and event friends form trivial relations. The mixture of important and
useless relations may introduce noise and produce negative results.
A study shows that SRS employing all relations produces more adverse results than tradi-
tional RS (Au Yeung and Iwata 2011). Users employ and express both negative and positive
relations. Positive relations are used extensively in most of the existing SRS. However, neg-
ative relations also exist in the form of dislike, suspicion or distrust. Further, most of the
social networks contain weighting data. The weights to links or edges indicate the closeness
or relationship strength between users. Weighted relations express more trust in generating
effective recommendations. A recent review of the literature on this area found that many
SRSs consider social connections to be of the same type. But in real world scenario, a user can
have different kinds of relations with different people in different spheres. Zhang et al. (2008)
examines the issues related to recommendations in heterogeneous networks and develops a
random walk model that approximates the importance of objects in these networks. They
propose a pair-wise learning algorithm to adjust the weights of different links in heteroge-
neous social networks. Tang et al. (2013) emphasize the need of heterogeneous strengths
for recommendation in local social context. Zhou et al. (2015) explain the benefits of users’
distrust data in improving the accuracy of SRS. Recently, Wang and Ma (2016) explain how
to add different weights to different users. The users who are more trustworthy and have high
recommendation competence are given high trust values.
Figure 1exemplifies user social relations in different social networks. For instance, user u1
may prefer to take advice from user u2for watching horror movie. But he may prefer to seek
an idea from user u5for attending a conference. The figure clearly demonstrates that users
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Fig. 1 An example of user social relations in different social networks
form different types of relations in different networks. The diversity in social connections
enhances SRS in providing efficient recommendations for diverse group of items.
4.8 Semantic filtering
Semantic filtering is a vital feature of RS to recommend important data by filtering the
unimportant one and improving the quality of RS (Codina and Ceccaroni 2010). Semantic
filtering alleviates the cold-start problem by exploiting semantic similarities to offer logical
recommendations. RS leveraging semantic filtering are competent in maintaining long-term
needs and preferences of users. With semantic technology, it becomes possible to recognize
real world entities from textual information and the properties of the information are used as
background knowledge (Lašek and Vojtáš 2011). Many researchers have studied the usage
of semantic web in RS. In social network analysis, it is essential to discover semantic social
relations to build meaningful relations. RSs require experts who may give exact answer
for the questions but finding appropriate experts for a specific domain is quite challenging.
To address this issue, Davoodi et al. (2013) propose a hybrid framework that integrates
content-based recommendation algorithms into semantic social network-based collaborative
algorithms to design an expert RS. The framework first creates the profile of experts and uses
the background knowledge from Wikipedia to enrich user profile. Then, it constructs a social
network of experts and finds communities of experts in the resultant semantic-based social
network. Finally,it uses the domain expertise from this network to generate recommendations.
Yang et al. (2013) tried to improve the accuracy of RSs by introducing semantic technology.
They integrated the semantic technology in RS based on Slop One scheme. Another work that
incorporated semantic technology in social networks is presented by Sellami et al. (2014).
This approach builds a semantic social network to generate semantic social recommendations.
Frikha et al. (2015) followed a different perspective to develop SRS, that is, an ontology-
oriented approach. They exploited user-interest ontology to semantically describe social
relations and interactions. The system is designed to generate useful items for users interested
in visiting Tunisian places. Sulieman et al. (2016) combine semantic information (i.e., item
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features and user preferences) and social information (i.e., social network analysis measures)
to propose a social-semantic RS. This system is a hybrid graph-based RS since it uses CF
technique to extract social information and CBF technique to extract semantic information.
Lastly, Zhang et al. (2017a) emphasize the problem of accessing explicit social information
and devise a method for deriving implicit and reliable social knowledge to identify the top-
k semantic friends for each user in system. The devised method called Collaborative User
Network Embedding (CUNE) is further extended to CUNE Matrix factorization (CUNE-MF)
and CUNE Bayesian Personal Ranking (CUNE-BPR) to solve the issues of rating prediction
and item rankings, respectively.
Table 2gives the brief classification of the above features of SRS.
5 Comparison of Social recommender systems
This section gives a brief comparison of different SRS proposed in recent years. We have
already discussed these systems above. Table 3compares various SRS on the basis of different
attributes like parameters, approach used, characteristics and metrics.
The metrics used by most of the systems are used for rating prediction and recommenda-
tion evaluation. In these metrics, MAE, MSE and RMSE are used as error measures whereas
Precision, Recall, F1-measure and MAP are used to evaluate recommendations. Cosine simi-
larity is a CF method that uses positive ratings for computation. Isinkaye et al. (2015)presents
different evaluation metrics for RS.
6 Future scope
A social recommender faces various challenges during recommendations. Though the need
to incorporate trust, tag, group, heterogeneous social connections, semantic filtering of infor-
mation, cross-social media data and temporal dynamics features in social recommenders has
been recurrently stated independently and even worked by many researchers. However, none
of them has included the combinations of these features to improve recommendations. There
are various challenges concerned with extracting relationships from social networks. Further
research should be undertaken in the following areas:
1. Cross-domain recommendation problems are closely related to link prediction issues.
Prediction of potential and missing links in social networks will enhance the efficiency
of SRS intensely.
2. An important challenge in SRS is the heterogeneous and unbalanced nature of social
relations where it may not be possible to retrieve all relation types. It is easy to gather
trust or distrust relations in some networks but may not be feasible to retrieve such
relationships in other networks. Signed networks contain both positive and negative
links. Recommendation in signed networks is also a frontier research area.
3. Privacy is a crucial issue for social network users as users feel reluctant to give confidential
details for fear of maltreatment.
4. There is requirement in tagging based RS to choose negative tags to illustrate the dislike
of user towards the items. Further, tags equality is another major concern in tag-based
SRS where a mixture of tags exists that are different in terms of words but similar in
meanings. The use of semantic methods can improve the tag equality.
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Table 2 Classification of features of social recommender systems
S.No. Features Description References
1. Context A parameter that describes the social, physical, mental
and emotional state of a user
Adomavicius and Tuzhilin (2011), Liu and Aberer
(2013), Liu et al. (2013a), Macedo et al. (2015),
Capdevila et al. (2016), Sassi et al. (2017), Liao et al.
(2018) and Colombo-Mendoza et al. (2018)
2. Trust This parameter defines the willingness of a user to rely
on others actions
Li et al. (2013), Gao et al. (2015), Shen et al. (2016),
Yang et al. (2017), Dang and Ignat (2017), Cui et al.
(2017), Tian and Liang (2017) and Shokeen and Rana
(2018b)
3. Tag A label that acts as a connection between a users and a
resource. It exposes preference of users
Chirita et al. (2007), Song et al. (2011), Pan et al.
(2012), Huang et al. (2014), Zhou et al. (2015),
Arnaboldi et al. (2016) and Xu et al. (2017)
4. Group This category detects groups in social networks to
provide recommendations
Masthoff (2011), Quijano-Sanchez et al. (2013),
Christensen et al. (2016), Guo et al. (2016), Hong
et al. (2017) and Gottapu and Monangi (2017)
5. Cross social media data This parameter identifies users registered on multiple
sites and then uses the data of one website to give
recommendations for other
Zhao et al. (2013), Li and Lin (2014), Jiang et al.
(2015), Wang et al. (2017), Khan et al. (2017)andMa
et al. (2018)
6. Temporal dynamics It accounts for the changes in need of people with time.
This parameter suggests when instead of what.
De Pessemier et al. (2010), Gao et al. (2013), Pham et al.
(2015), Zhang et al. (2017b), Kefalas et al. (2018),
Gurini et al. (2018) and Shokeen and Rana (2018a)
7. Heterogeneous social connections The diversity in user relations in different networks Zhang et al. (2008), Au Yeung and Iwata (2011), Tang
et al. (2013), Zhou et al. (2015) and Wang and Ma
(2016)
8. Semantic filtering The parameter filters out the irrelevant data to give
meaningful results
Davoodi et al. (2013), Yang et al. (2013), Sellami et al.
(2014), Frikha et al. (2015), Sulieman et al. (2016)
and Zhang et al. (2017a)
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Table 3 Comparison of social recommender systems
System Parameters Approach Characteristics Metrics
SoCo (Liu et al. 2013b) Contextual information,
Social regularization
Matrix factorization Context-based
recommendation
RMSE, MAE
dTrust (Dang and Ignat 2017) Topology of trust-user-item
network
Deep neural network Rating prediction RMSE, MAE
HDLPR-NS (Xu et al. 2017) Tags, user profile, negative
sampling
Deep neural network Tag-based personalized
recommendation
Precision, recall, F1 measure
GeoSRS (Capdevila et al. 2016) Sentiment analysis+text
modelling
Hybrid (CF+CBF) Location-based
recommendation
Cosine-based similarity,
accuracy, coverage
TT-CDR (Ma et al. 2018) Social trust, context
dependence, ratings
Matrix factorization Cross-domain
recommendation
Recall, MAE
HWR (Jiang et al. 2015) User behavior, social ties Random walk Cross-domain
recommendation
MAE, RMSE, Precision,
recall, F1 measure, MAP
SR-FR, TLDA-FR (Zhang et al. 2017b) Time decay function, social
relations, interest similarity
Hybrid algorithm Friend recommendation MAP
MAE Mean Accuracy Error, MAP Mean Accuracy Precision, RMSE Root Mean Square Error
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5. Community detection in social networks is analogous to RS. Communities are detected to
observe the insights of network. A number of community detection algorithms have been
developed to analyze social networks. Community-based recommendations solve various
issues of RSs such as cold-start, scalability, data sparsity and serendipity. Overlapping
community detection algorithms (Shokeen et al. 2019) handling extensive heterogeneous
networks is another area of research in SRS.
6. The selection of relevant social network data for SRS is another research direction. The
selected data should be capable enough to defeat the occurrence of attacks like shilling
attacks where users give positive responses to their items and negative recommendations
to items of their competitors.
7. Group members in a social network are easily influenced by their peers in the group.
The adapting nature of user’s rating behavior influences relationship between users in a
community which is a part of discussion in SRS. The challenge arises when preferences
in a group become inconsistent.
8. Recommendations can be powerful enough to modify the preferences of entire network
or structure. This is due to the fact that social network are dynamic in nature. For illus-
tration, recommending similar news to users can sometimes change the preference of
probably whole network. Such recommendations are network-centric. Social media has
also become a platform for people to disseminate fake news. Machine learning and other
algorithms can be used to combat, detect and mitigate fake news which otherwise could
have negative effects on the society.
9. It is observed that under-contribution is often a problem in online social environment,
that is, people rarely like to contribute in giving ideas and comments. Instead users prefer
to use the cooked and processed information, which is known as social loafing. Studies
demonstrate that users like to contribute more when they think that their contributions
are unique and beneficial to communities or when they particularly like some group.
7 Conclusion
Social networks are excellent platforms that serve a dual purpose. Firstly, social networks
generate recommendations using the rich information available. Secondly, social networks
impart these recommendations to users. This paper categorizes the current research in SRS
into eight spheres on the basis of features, namely context, trust, tag, group, cross social media
information, temporal dynamics, heterogeneous social connections and semantic filtering. We
have given the explanation of these features for use in SRS and also discussed the work done
in corresponding spheres. The goal of incorporating these features is to achieve high social
recommendation accuracy. We may also select the interest, profiles and recommendations
of some specific people to be used by the recommendation engine. A good RS uses an
array of approaches to conclude items to users with an evident probability. The selection
of an algorithm is extremely based on choosing the constraints which can fit to the given
condition. Our study provides encouragement for a new way to implement these features
in SRS. In addition, we also suggest some future challenges in this field. For the future
work, we would like to use a combination of techniques to develop a hybrid model for social
recommendation that would incorporate a set of these features.
Acknowledgements The first author of the paper likes to say thanks to Council of Scientific and Industrial
Research (CSIR) to receive financial assistance in the form of JRF.
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