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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 80–90Cite as

Locality-Sensitive Linear Bandit Model for Online Social Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

Abstract

Recommender systems provide personalized suggestions by learning users’ preference based on their historical feedback. To alleviate the heavy relying on historical data, several online recommendation methods are recently proposed and have shown the effectiveness in solving data sparsity and cold start problems in recommender systems. However, existing online recommendation methods neglect the use of social connections among users, which has been proven as an effective way to improve recommendation accuracy in offline settings. In this paper, we investigate how to leverage social connections to improve online recommendation performance. In particular, we formulate the online social recommendation task as a contextual bandit problem and propose a Locality-sensitive Linear Bandit (LS.Lin) method to solve it. The proposed model incorporates users’ local social relations into a linear contextual bandit model and is capable to deal with the dynamic changes of user preference and the network structure. We provide a theoretical analysis to the proposed LS.Lin method and then demonstrate its improved performance for online social recommendation in empirical studies compared with baseline methods.

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Acknowledgments

The work described in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CUHK 14208815 of the General Research Fund), and 2015 Microsoft Research Asia Collaborative Research Program (Project No. FY16-RES-THEME-005).

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Authors and Affiliations

  1. Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China

    Tong Zhao & Irwin King

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Tong Zhao & Irwin King

Authors
  1. Tong Zhao
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  2. Irwin King
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Corresponding author

Correspondence to Tong Zhao .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Zhao, T., King, I. (2016). Locality-Sensitive Linear Bandit Model for Online Social Recommendation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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