Joint Social and Content Recommendation for User-Generated Videos in Online Social Network

Online social network is emerging as a promising alternative for users to directly access video contents. By allowing users to import videos and re-share them through the social connections, a large number of videos are available to users in the online social network. The rapid growth of the user-generated videos provides enormous potential for users to find the ones that interest them; while the convergence of online social network service and online video sharing service makes it possible to perform recommendation using social factors and content factors jointly. In this paper, we design a joint social-content recommendation framework to suggest users which videos to import or re-share in the online social network. In this framework, we first propose a user-content matrix update approach which updates and fills in cold user-video entries to provide the foundations for the recommendation. Then, based on the updated user-content matrix, we construct a joint social-content space to measure the relevance between users and videos, which can provide a high accuracy for video importing and re-sharing recommendation. We conduct experiments using real traces from Tencent Weibo and Youku to verify our algorithm and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.

[1]  Rynson W. H. Lau,et al.  Multimedia and Signal Processing , 2012, Communications in Computer and Information Science.

[2]  S. Floyd,et al.  Adaptive Web , 1997 .

[3]  Lifeng Sun,et al.  Propagation-based social-aware replication for social video contents , 2012, ACM Multimedia.

[4]  Lifeng Sun,et al.  Prefetching strategy in peer-assisted social video streaming , 2011, MM '11.

[5]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[6]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[7]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[8]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[9]  Thomas Hofmann,et al.  Unifying collaborative and content-based filtering , 2004, ICML.

[10]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[11]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[12]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[13]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[14]  Balachander Krishnamurthy,et al.  A few chirps about twitter , 2008, WOSN '08.

[15]  Shankar Kumar,et al.  Video suggestion and discovery for youtube: taking random walks through the view graph , 2008, WWW.

[16]  C. Richard Johnson,et al.  Matrix Completion Problems: A Survey , 1990 .

[17]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[18]  R. Schatz,et al.  Mobile TV Becomes Social - Integrating Content with Communications , 2007, 2007 29th International Conference on Information Technology Interfaces.

[19]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[20]  Lifeng Sun,et al.  Group Recommendation Using External Followee for Social TV , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[21]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[22]  Pabitra Mitra,et al.  Feature weighting in content based recommendation system using social network analysis , 2008, WWW.

[23]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[24]  D. Watts,et al.  A generalized model of social and biological contagion. , 2005, Journal of theoretical biology.

[25]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[26]  Lixin Gao,et al.  The impact of YouTube recommendation system on video views , 2010, IMC '10.

[27]  Lora Oehlberg,et al.  Social TV: Designing for Distributed, Sociable Television Viewing , 2008, Int. J. Hum. Comput. Interact..

[28]  Virgílio A. F. Almeida,et al.  Characterizing user behavior in online social networks , 2009, IMC '09.

[29]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[30]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[31]  Stratis Ioannidis,et al.  Distributed rating prediction in user generated content streams , 2011, RecSys '11.

[32]  Stefano Battiston,et al.  A model of a trust-based recommendation system on a social network , 2006, Autonomous Agents and Multi-Agent Systems.

[33]  Samer Faraj,et al.  Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice , 2005, MIS Q..

[34]  Vahab S. Mirrokni,et al.  Optimal marketing strategies over social networks , 2008, WWW.

[35]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.