[Lecture Notes in Electrical Engineering] IT Convergence and Services Volume 107 || Collaborative Filtering Recommender System Based on Social Network

Collaborative Filtering RecommenderSystem Based on Social Network

Soo-Cheol Kim, Jung-Wan Ko, Jung-Sik choand Sung Kwon Kim

Abstract In recent years, the use of social network services is constantlyincreasing. A social network service (SNS) is an individual-centered online servicethat provides means for users to share information and interact over the Internet.In a SNS, recommender systems supporting filtering of substantial quantities ofdata are essential. Collaborative filtering (CF) used in recommender systemsproduces predictions about the interests of a user by collecting preferences or tasteinformation from many users. The disadvantage with the CF approach is that itproduces recommendations relying on the opinions of a larger community (i.e.,recommendations are determined based on what a much larger community thinksof an item). To address this problem, this article exploits social relations betweenpeople in a social network. That is, the recommender system proposed in thisarticle takes into account social relations between users in performing collabora-tive filtering. The performance of the proposed recommender system was evalu-ated using the mean absolute error.

Keywords Collaborative filtering � Recommendation system � Social network

S.-C. Kim � J.-W. Ko � J.-S. cho � S. K. Kim (&)Computer Science and Engineering, Chung-Ang University, Seoul, Koreae-mail: [email protected]

S.-C. Kime-mail: [email protected]

J.-W. Koe-mail: [email protected]

J.-S. choe-mail: [email protected]

J. J. Park et al. (eds.), IT Convergence and Services,Lecture Notes in Electrical Engineering 108, DOI: 10.1007/978-94-007-2598-0_53,� Springer Science+Business Media B.V. 2012

503

1 Introduction

In today’s online environments, there exist a variety of social networks made up ofindividuals or groups called ‘‘nodes’’, which are tied by one or more specific typesof interdependency. Like real-world social structures, people are connected to eachother in an online social network through many kinds of social relations. Forexample, various communities and organizations are freely created and run inweb-based social networking sites such as Twitter, Facebook, Epinions, Myspaceand Cyworld.

In a SNS, a large amount of information on users’ behavior, activity or pref-erences is created. Note that not all of such information is trustworthy becauseanybody, who might intentionally or unintentionally supply false information, canparticipate in a SNS. Hence, recommender systems that help users find informationby providing recommendations play a significant role in a SNS [1, 2].

Recommender systems use a specific type of information filtering approachsuch as content-based filtering, demographic filtering and collaborative filtering.Collaborative filtering used in the recommender system proposed in this articlerecommends items or users. In item predictions (filtering), items that like-mindedusers rated as of great value are measured for similarity to identify the set of itemsto be recommended. This technique does not support the social process of asking atrustworthy friend for a recommendation. The disadvantage of the collaborativefiltering approach is that recommendations are made depending on the opinions ofothers irrespective of their trustworthiness. This approach produces standardized(non-specific) recommendations because the items that are favored by a largercommunity are constantly recommended, used, and reviewed while other itemshave little chance to be considered. In such an approach, a truly personalized viewof an item using the opinions most appropriate for a given user is less likely to bedeveloped. To resolve this problem, the proposed recommender system findstrustworthy users using social relations in an online social network and performscollaborative filtering with the users weighted by trustworthiness.

In the proposed collaborative filtering recommender system, the Friend of aFriend (FOAF), breadth-first search (BFS) and user’s social recognition in thesocial network are used to connect the users (nodes) of an online social network.The Epinions dataset was used to implement the proposed recommender system.In the social network created using the Epinions dataset, social relations betweenusers are analyzed and trustworthy users are found by computing the distancebetween users. The proposed system performs collaborative filtering using theidentified trustworthy users [3].

The rest of the article is organized as follows. Section 2 gives a briefdescription of social networks, recommender systems and collaborative filtering.Section 3 presents the proposed recommender system that exploits social relationsbetween users in a social network in order to improve the performance of thetraditional collaborative filtering system. In Sect. 4, the proposed collaborative

504 S.-C. Kim et al.

filtering recommender system based on social networks is compared to the con-ventional collaborative filtering system. Finally, Sect. 5 concludes the article.

2 Related Work

2.1 Social Network and Friend of a Friend (FOAF)

A social network service provides means to connect with friends and to shareopinions with others. Most social network services are web based and focus onbuilding social relations between people, who share interests and activities.Friendship and social recognition created on social networking sites are importantsocial factors to be considered in recommender systems. In friendship, distantfriends linked via the FOAF as well as direct friends are considered. Social rec-ognition, the value that an individual gets from the social network, is determinedby the number of friends that the individual has in the network. Friendship andsocial recognition can be used to identify trustworthy users for a given user in asocial network [4].

2.2 Recommender System and Collaborative Filtering

A recommender system recommends items or users that are likely to be of interestto the user based on predefined similarity measures. The recommender systemproposed in this article recommends items using the collaborative filtering tech-nique. For item recommendations, the collaborative filtering technique first looksfor like-minded users and makes predictions (filtering) about the interests of theuser using the ratings from those like-minded users [5].

2.3 Breadth First Search (BFS)

In computer science, breadth-first search (BFS) is a graph search algorithm thatbegins at the root node and explores all the neighboring nodes. Then for each ofthose nearest nodes, it explores their unexplored neighbor nodes, and so on, until itfinds the goal. The BFS can be used to create a social network graph as the set ofnodes reached by the BFS form the connected component containing the startingnode. The recommender system proposed in this article employs the BFS to createa graph made up of users in a SNS and computes trustworthiness between users inthe created graph.

Collaborative Filtering Recommender System Based on Social Network 505

3 Proposed Method

Figure 1 shows the conceptual structure of the proposed recommender system. Inthe proposed system, the conventional collaborative filtering technique isenhanced by analyzing social relations between users in a SNS and identifyingtrustworthy users that are referred to for item recommendations. The red arrowedline in Fig. 1 highlights that the BFS algorithm adopted in the proposed systemdetermines the trust value by taking into account both user relations and rating data.

3.1 Identification of Trustworthy Users in a Social

There can be many kinds of ties between the nodes in a social network that is adirected graph. Figure 2 depicts four types of ties: fan, friend, follower andmember. The ‘fan’ relationship represents that a given user trust another user,whereas the ‘follower’ relationship represents that the given user is trusted byanother user. The ‘friend’ relationship represents that the concerned two usershave mutual trust (a two-way tie). In the ‘member’ relationship, the organization towhich a given user belongs is considered trustworthy. Figure 3 illustrates theidentification of trustworthy users based on the social network graph components(nodes and ties) and rating data.

The U � U array in Fig. 3 represents the trust level between users and the U � Iarray represents the user’s rating score for the items. The adopted BFS algo-rithm searches through every connected node of a given user in the directed graph.When there is more than one user (node) at the same depth and directed toward asame node, the user that has rated more items is chosen by referring to the U � Iarray.

Fig. 1 Proposedrecommender systemarchitecture


BFSðx;yÞ ¼ BFSðx;Maxðy preÞÞ þ 1 ð1Þ

awareðyÞ ¼fany þ followery

2ðn� 1Þ ð2Þ

TrustSðx;yÞ ¼Nj j � BFSðx;yÞ

Nj j � aþ awareðyÞ � b ð3Þ

In Eq. 1, the distance between users is measured using the BFS algorithm.BFSðx;yÞ denotes the measured distance between user x and y: Maxðy preÞ in Eq. 1denotes that when there is more than one user node at the same depth and directedtoward a same node, the one with a higher number of rated items is chosen tocompute BFSðx;yÞ: Equation 2 counts fan and follower of a given user and dividesthe counted number by the number of users so as to compute social recognitionthat the user has earned in the social network. In Eq. 3, the social relation measuresobtained in Eqs. 1 to 2 are transformed into a normalized value in the range

Fig. 2 Relationships in a social network

Fig. 3 Computation of trustworthy users in a social network


between 0 and 1. TrustSðx;yÞ represents the trustworthiness between user x and y—value 1 indicates that a given user has a close relationship with the other user whois highly recognized in the social network.

3.2 Recommendation System and Collaborative Filtering

Sðx;yÞ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Pna¼1 fðx;aÞ fðy;aÞ

q

� TrustSðx;yÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Pna¼1 ðfðx;aÞÞ

2q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Pna¼1 ðfðy;aÞÞ

2q ð4Þ

In Eq. 4, Sðx;yÞ represents similarity between items rated by user x and y: a denotesthe items examined for similarity and n is the total number of the items.fðx;aÞ and fðy;aÞ denote the ratings of item a by user x and y; respectively. Theconventional collaborative filtering is extended by adding weight denoting theweight given to the similarity computation according to the trust level betweenusers.

Uðx;aÞ ¼ rx þPn

y¼1 Sðx;yÞ fðy;aÞPn

y¼1 Sðx;yÞð5Þ

Uðx;aÞ is the predicted rating (preference) of item a by user x (item a has notyet been rated by user x). rx denotes the average rating of items by user x: Sðx;yÞ isthe measured item similarity associated with user x and y: fðy;aÞ represents therating of item a by user y. n denotes the number of neighboring nodes to beconsidered.

4 Experiments and Evaluation

4.1 Experimental Data

To perform the experiments with SNS data, the dataset from Epinions.com, ageneral consumer review site, was used. In the Epinions dataset, the number ofusers was 49,290 and the number of items was 139,738. The number of ratings ofthe items was 664,824. The Epinions dataset has 487,181 social ties. Thenumerical ratings of an item are in the range {1, 5}. In terms of social relation,value 1 represents that there is a relationship between users. The absence of thevalue indicates that there is no relationship. Social relations between users aredirected (i.e., they are represented with a directed edge in the graph).


4.2 Experimental Method

In order to increase data accuracy, the volume of the Epinions dataset was reducedto 1/1000, and data for training and testing was randomly divided (the ratio of thedata used for training to testing was 8:2). This operation was repeated five times inthe experiments. The performance of the proposed social network-based recom-mender system was compared to that of the traditional collaborative filteringsystem. There are several ways to evaluate a recommender system. In this work,the mean absolute error (MAE) was used.

MAE ¼Pn

i¼1 ra;i � ra;i

�

�

�

�

nð6Þ

ra;i denotes the actual rating of an item by the user and ra;i denotes the user’srating predicted by the recommender system. n is the number of items evaluated.The recommender system is ‘good’ (i.e., prediction is accurate) as the resultingvalue is close to 0.

4.3 Performance Evaluation

To evaluate the performance of the proposed recommender system, it was com-pared to the conventional collaborative filtering system. The performance of theproposed and conventional collaborative filtering systems was represented inMAE, and it was measured five times (e.g., the operation of randomly dividing thedataset for training and testing was repeated five times) (Fig. 4).

Overall, the MAE values are greater than 1, which indicates that the perfor-mance of the compared recommender systems is not high. In the first and thirdperformance measures, the proposed system has lower performance (higher MAEvalues) than the conventional collaborative filtering system. On the other hand, the

1.048

1.05

1.052

1.054

1.056

1.058

1.06

1.062

1 2 3 4 5

CF

SNS+CF

Fig. 4 Performancecomparison (proposed vs.conventional collaborativefiltering)


proposed system performs better than the conventional collaborative filteringsystem in the second, fourth and fifth measures. The performance evaluation hereshows that the proposed recommender system is a solution differed from thetraditional collaborative filtering system.

5 Conclusion

This article proposes a social network-based recommender system to solve theproblem of relying on the opinions of a larger community in the traditional col-laborative filtering technique. In the proposed system, trustworthy users identifiedby analyzing social relations between users in a social network are used to rec-ommend items. A drawback of the proposed recommender system is that socialrelations in the range {0, 1} and the range {-1, 0} are not clearly distinguished dueto the use of weight values in the range between 0 and 1. In addition, the BFSalgorithm adopted in the proposed system exhaustively searches the entire graph,so it takes a long time to yield recommendations. Trustworthy users that theproposed system identifies for item recommendations differ substantially fromsimilar (or like-minded) users found in the traditional recommender system tomake recommendations. In the future, a way to reduce the computational load ofthe proposed recommender system will be studied to be applicable in mobileenvironments.

Acknowledgments This work was supported by Basic Science Research Programs through theNational Research Foundation of Korea (NRF) grand funded by the Korea government (MEST)(No.2010-0013121).

References

1. Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links inonline social networks. In: WWW 2010, April

2. Adamic L, Adar E (2005) How to search a social network. In: HP Labs3. Massa P, Avesani P (2007) Trust-aware recommender systems. In: RecSys’07, October4. Symeonidis P, Tiakas E, Manolopoulos Y (2010) Transitive node similarity for link prediction

in social networks with positive and negative links. In: RecSys2010, September5. Zhang Z, Wang X-M, Y-X Wang (2005) A P2P global trust model based on recommedation.

In: Proceedings of the fourth international conference on machine learning and cybernetics,August


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[Lecture Notes in Electrical Engineering] IT Convergence and Services Volume 107 || Collaborative Filtering Recommender System Based on Social Network