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Enhancing online video recommendation using social user interactions

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Abstract

The creation of media sharing communities has resulted in the astonishing increase of digital videos, and their wide applications in the domains like online news broadcasting, entertainment and advertisement. The improvement of these applications relies on effective solutions for social user access to videos. This fact has driven the research interest in the recommendation in shared communities. Though effort has been put into social video recommendation, the contextual information on social users has not been well exploited for effective recommendation. Motivated by this, in this paper, we propose a novel approach based on the video content and user information for the recommendation in shared communities. A new solution is developed by allowing batch video recommendation to multiple new users and optimizing the subcommunity extraction. We first propose an effective technique that reduces the subgraph partition cost based on graph decomposition and reconstruction for efficient subcommunity extraction. Then, we design a summarization-based algorithm which groups the clicked videos of multiple unregistered users and simultaneously provide recommendation to each of them. Finally, we present a nontrivial social updates maintenance approach for social data based on user connection summarization. We evaluate the performance of our solution over a large dataset considering different strategies for group video recommendation in sharing communities.

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Acknowledgements

The research presented in this paper has been supported via ARC projects DP140100841, DP150103071 and DP130102691, NSFC project 61332013, the Hong Kong SRFDP&RGC ERG Joint Research Scheme MHKUST602/12, National Grand Fundamental Research 973 Program of China under Grant 2014CB340303, Microsoft Research Asia Gift Grant and Google Faculty Award 2013.

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Authors and Affiliations

  1. School of Computer Science and Info Tech, RMIT University, Melbourne, Australia

    Xiangmin Zhou & Dong Qin

  2. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hongkong, China

    Lei Chen

  3. Centre for Applied Informatics, Victoria University, Footscray, Australia

    Yanchun Zhang

  4. School of Computer Science, Fudan University, Shanghai, China

    Yanchun Zhang

  5. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Longbing Cao

  6. School of Information Technology, Deakin University, Burwood, Australia

    Guangyan Huang

  7. Data61, CSIRO, Sydney, Australia

    Chen Wang

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  1. Xiangmin Zhou
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  2. Lei Chen
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  7. Chen Wang
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Correspondence to Xiangmin Zhou.

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Zhou, X., Chen, L., Zhang, Y. et al. Enhancing online video recommendation using social user interactions. The VLDB Journal 26, 637–656 (2017). https://doi.org/10.1007/s00778-017-0469-2

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