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ABPR-- A New Way of Point-of-Interest Recommendation via Geographical and Category Influence

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 902))

Abstract

Point-of-Interest (POI) recommendation has been an important topic on Location-Based Social Networks (LBSN). It could recommend the POI point for users that they have never been. During the latest research, when adding geographical influence, recent research always pick up all the POI points to learn the influence it makes to the users. However, this may reduce the precision of experiment, for it does not take into consideration the reason that influences users in their frequent check-in activity region. To solve this problem, we propose a new POI recommending approach with the activity region, named Activity region Bayesian Personalized Ranking (ABPR), which adds geographical influence into the basket of BPR. This paper outlines the experiments done with Gowalla and Foursquare datasets to demonstrate the effectiveness and advantage of our approach.

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Author information

Authors and Affiliations

  1. Key Laboratory of Database, Heilongjiang University, Harbin, China

    Jingyuan Gao & Yan Yang

Authors
  1. Jingyuan Gao
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  2. Yan Yang
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Corresponding author

Correspondence to Yan Yang .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Xidian University, Xi’an, Shaanxi, China

    Qiguang Miao

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Harbin University of Science and Technology, Harbin, China

    Wei Xie

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2018 Springer Nature Singapore Pte Ltd.

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Gao, J., Yang, Y. (2018). ABPR-- A New Way of Point-of-Interest Recommendation via Geographical and Category Influence. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_9

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  • DOI: https://doi.org/10.1007/978-981-13-2206-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2205-1

  • Online ISBN: 978-981-13-2206-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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