Joint social and content recommendation for user-generated videos in online social network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

90 Scopus Citations
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Author(s)

  • Zhi Wang
  • Lifeng Sun
  • Wenwu Zhu
  • Shiqiang Yang
  • Hongzhi Li

Detail(s)

Original languageEnglish
Article number6397621
Pages (from-to)698-709
Journal / PublicationIEEE Transactions on Multimedia
Volume15
Issue number3
Publication statusPublished - Apr 2013
Externally publishedYes

Abstract

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. © 2012 IEEE.

Research Area(s)

  • Online social network, Social propagation, Video recommendation

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.

Citation Format(s)

Joint social and content recommendation for user-generated videos in online social network. / Wang, Zhi; Sun, Lifeng; Zhu, Wenwu; Yang, Shiqiang; Li, Hongzhi; Wu, Dapeng.

In: IEEE Transactions on Multimedia, Vol. 15, No. 3, 6397621, 04.2013, p. 698-709.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review