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Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Point-of-interest (POI) recommendation that suggests new locations for people to visit is an important application in location-based social networks (LBSNs). Compared with traditional recommendation problems, e.g., movie recommendation, geographical influence is a special feature that plays an important role in recommending POIs. Various methods that incorporate geographical influence into collaborative filtering techniques have recently been proposed for POI recommendation. However, previous geographical models have struggled with a problem of geographically noisy POIs, defined as POIs that follow the geographical influence but do not satisfy users’ preferences. We observe that users in the same geographical region share many POIs, and thus we propose the co-geographical influence to filter geographically noisy POIs. Furthermore, we propose the Geo-Pairwise Ranking Matrix Factorization (Geo-PRMF) model for POI recommendation, which incorporates co-geographical influence into a personalized pairwise preference ranking matrix factorization model. We conduct experiments on two real-life datasets, i.e., Foursquare and Gowalla, and the experimental results reveal that the proposed approach outperforms state-of-the-art models.

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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 (Nos. CUHK 14203314 and CUHK 14234416 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 Application, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China

    Shenglin Zhao, Irwin King & Michael R. Lyu

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

    Shenglin Zhao, Irwin King & Michael R. Lyu

Authors
  1. Shenglin Zhao
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  2. Irwin King
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  3. Michael R. Lyu
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Corresponding author

Correspondence to Shenglin Zhao .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Zhao, S., King, I., Lyu, M.R. (2017). Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_37

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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