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International Conference on Web-Age Information Management

WAIM 2016: Web-Age Information Management pp 178–190Cite as

Point-of-Interest Recommendations by Unifying Multiple Correlations

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9658))

Abstract

In recent years, we have witnessed the development of location-based services which benefit users and businesses. This paper aims to provide a unified framework for location-aware recommender systems with the consideration of social influence, categorical influence and geographical influence for users’ preference. In the framework, we model the three types of information as functions following a power-law distribution, respectively. And then we unify different information in a framework and learn the exact function by using gradient descent methods. The experimental results on real-world data sets show that our recommendations are more effective than baseline methods.

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Author information

Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, Beijing, 100024, China

    Ce Cheng, Jiajin Huang & Ning Zhong

  2. Deptartment of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 371-0816, Japan

    Ning Zhong

Authors
  1. Ce Cheng
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  2. Jiajin Huang
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  3. Ning Zhong
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Corresponding author

Correspondence to Ce Cheng .

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University, Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Cheng, C., Huang, J., Zhong, N. (2016). Point-of-Interest Recommendations by Unifying Multiple Correlations. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-39937-9_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39936-2

  • Online ISBN: 978-3-319-39937-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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