Exploring sharing patterns for video recommendation on YouTube-like social media

Xiaoqiang Ma, Haiyang Wang, Haitao Li, Jiangchuan Liu, Hongbo Jiang

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.

Original languageEnglish (US)
Pages (from-to)675-691
Number of pages17
JournalMultimedia Systems
Volume20
Issue number6
DOIs
StatePublished - Oct 18 2014
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013, Springer-Verlag Berlin Heidelberg.

Keywords

  • Social media
  • Social similarity
  • Video recommendation

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