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Point-Of-Interest Recommendation Using Temporal Orientations of Users and Locations

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9642))

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Abstract

Location Based Social Networks (LBSN) promotes communications among subscribers. Utilizing online check-in data supplied via LBSN, Point-Of-Interest (POI) recommendation systems propose unvisited relevant venues to the users. Various techniques have been designed for POI recommendation systems. However, diverse temporal information has not been studied adequately. From temporal perspective, as visited locations during weekday and weekend are marginally different, we choose weekly intervals to improve effectiveness of POI recommenders. However, our method is also applicable to other similar periodic intervals. People usually visit tourist and leisure spots during weekends and work related places during weekdays. Similarly, some users perform check-ins mostly during weekend, while others prefer weekday predominantly. In this paper, we define a new problem to perform recommendation, based on temporal weekly alignments of users and POIs. We argue that locations with higher popularity should be more influential. Therefore, In order to solve the problem, we develop a probabilistic model which initially detects a user’s temporal orientation based on visibility weights of POIs visited by her. As a step further, we develop a recommender framework that proposes proper POIs to the user according to her temporal weekly preference. Moreover, we take succeeding POI pairs visited by the same user into consideration to develop a more efficient temporal model to handle geographical information. Extensive experimental results on two large-scale LBSN datasets verify that our method outperforms current state-of-the-art recommendation techniques.

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Notes

  1. 1.

    http://www.public.asu.edu/~hgao16/.

  2. 2.

    https://snap.stanford.edu/data/loc-brightkite.html.

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Acknowledgements

The authors wish to thank Prof. Xiaofang Zhou and Prof. Shazia Sadiq from The University of Queensland for their advice. This work is supported by Australian Research Council (ARC). Grant Number DP140103171.

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Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Saeid Hosseini & Lei Thor Li

Authors
  1. Saeid Hosseini
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  2. Lei Thor Li
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Correspondence to Saeid Hosseini .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology, Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas, Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota, Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University, Beijing, China

    Xiaoyong Du

  5. Fudan University, Shanghai, China

    X. Sean Wang

  6. Rutgers, The State University of New Jer, New Brunswick, New Jersey, USA

    Hui Xiong

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Hosseini, S., Li, L.T. (2016). Point-Of-Interest Recommendation Using Temporal Orientations of Users and Locations. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-32025-0_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32024-3

  • Online ISBN: 978-3-319-32025-0

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