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Video recommendation over multiple information sources

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Abstract

Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation. Our approach regards video recommendation as a ranking task. First, it generates multiple ranking lists by exploring different information sources. In particular, one novel source user’s relationship strength is inferred through the online social network and applied to recommend videos. Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation. It is shown that our scheme is flexible that can easily incorporate other methods by adding their generated ranking lists into our multi-task rank aggregation approach. We conduct experiments on a large dataset with 76 users and more than 11,000 videos. The experimental results demonstrate the feasibility and effectiveness of our approach.

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Notes

  1. http://www.youtube.com.

  2. http://video.yahoo.com.

  3. http://www.bing.com/videos/browse.

  4. http://www.facebook.com.

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Acknowledgments

This work was supported by the Innovation Scholarship for Ph.D. students at Beihang University under research grant (YWF-12-RBYJ-012), the National Natural Science Foundation of China (61170189, 60973105), the Fund of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2011ZX-03 and the Singapore National Research Foundation & Interactive Digital Media R&D Program Office, MDA under research grant (WBS:R-252-300-001-490). The authors would like to thank the editors and the anonymous reviewers for their valuable comments and remarks on this paper.

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Xiaojian Zhao & Zhoujun Li

  2. School of Computing, National University of Singapore, Singapore, 117590, Singapore

    Jin Yuan, Guangda Li & Tat-Seng Chua

  3. School of Computer and Information, Hefei University of Technology, Hefei, 230009, Anhui, China

    Meng Wang & Richang Hong

Authors
  1. Xiaojian Zhao
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  2. Jin Yuan
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  3. Meng Wang
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  4. Guangda Li
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  5. Richang Hong
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  6. Zhoujun Li
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  7. Tat-Seng Chua
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Correspondence to Meng Wang.

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Zhao, X., Yuan, J., Wang, M. et al. Video recommendation over multiple information sources. Multimedia Systems 19, 3–15 (2013). https://doi.org/10.1007/s00530-012-0267-z

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