Group Recommendation Using External Follwee for Social Tv
GROUP RECOMMENDATION
USING EXTERNAL FOLLOWEE FOR SOCIAL TV
XiaoyanWang1, Lifeng Sun1, ZhiWang1 and Da Meng2
1
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Department of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
1
muyushiok@gmail.com, 1sunlf@tsinghua.edu.cn, 1wangzhi04@mails.tsinghua.edu.cn, 2mengda0710@126.com
2
Abstract—Group recommendation plays a significant role in
Social TV systems, where online friends form into temporary groups to enjoy watching video together and interact with each other. Online microblogging systems introduce the "following" relationship that reflects the common interests between users in a group and external representative followees outside the group. Traditional group recommendation only considers internal group members’ preferences and their relationship. In our study, we measure the external followees’ impact on group interest and establish group preference model based on external experts’ guidance for group recommendation. In addition, we take advantage of the current watching video to improve context-aware recommendations. Experimental results show that our solution works much better in situations of high group dynamic and inactive group members than traditional approaches.
Keywords- Social Media, Group Recommendation, External
Expert, Context Filtering
I.
INTRODUCTION
The rapid development of user generated content (UGC) and social network results in a large number of social media content. Media content related with social connections differs from traditional ones concerning that sources of social media tend to be generated from friends’ sharing and recommender systems in social networks (SNS). User interaction in SNS continues to strengthen, followed by relationship expansion. Social TV brings a new trend of combining video streaming service with social network service, which has deeply
USING EXTERNAL FOLLOWEE FOR SOCIAL TV
XiaoyanWang1, Lifeng Sun1, ZhiWang1 and Da Meng2
1
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Department of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
1
muyushiok@gmail.com, 1sunlf@tsinghua.edu.cn, 1wangzhi04@mails.tsinghua.edu.cn, 2mengda0710@126.com
2
Abstract—Group recommendation plays a significant role in
Social TV systems, where online friends form into temporary groups to enjoy watching video together and interact with each other. Online microblogging systems introduce the "following" relationship that reflects the common interests between users in a group and external representative followees outside the group. Traditional group recommendation only considers internal group members’ preferences and their relationship. In our study, we measure the external followees’ impact on group interest and establish group preference model based on external experts’ guidance for group recommendation. In addition, we take advantage of the current watching video to improve context-aware recommendations. Experimental results show that our solution works much better in situations of high group dynamic and inactive group members than traditional approaches.
Keywords- Social Media, Group Recommendation, External
Expert, Context Filtering
I.
INTRODUCTION
The rapid development of user generated content (UGC) and social network results in a large number of social media content. Media content related with social connections differs from traditional ones concerning that sources of social media tend to be generated from friends’ sharing and recommender systems in social networks (SNS). User interaction in SNS continues to strengthen, followed by relationship expansion. Social TV brings a new trend of combining video streaming service with social network service, which has deeply
References: Proceedings of the Networked and Electronic Media Summit (NEM Summit 2011), Torino, Italy, pp of the SIGMM Workshop on Social Media, pp.85-94. 2009. Computer-Supported Cooperative Work, 2001. A. Jameson and B. Smyth. Recommendation to groups. The adaptive web: methods and strategies of web personalization, 2007. ACM. 2006 T.Kanungo, D.M.Mount, N.S.Netanyahu, C.D.Piatko and A.Y.Wu. Intelligence, Vol.24, NO.7, 2002.