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P-LAG: Location-Aware Group Recommendation for Passive Users

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Advances in Spatial and Temporal Databases (SSTD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10411))

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Abstract

Consider a group of users who would like to meet to a place in order to participate in an activity together (e.g., meet at a restaurant to dine). Such meeting point queries have been studied in the context of spatial databases, where typically the suggested points are the ones that minimize an aggregate traveling distance. Recently, meeting point queries have been enriched to take as input, besides the locations of users, also some preference criteria (e.g., expressed by some keywords). However, in many applications, a group of users may require a meeting point recommendation without explicitly specifying any preferences. Motivated by this, we study this scenario of group recommendation for such passive users. We use topic modeling to infer the preferences of the group on the different points of interest and combine these preferences with the aggregate spatial distance of the group members to the candidate points for recommendation in a unified search model. Then, we propose an extension of the R-tree index, called TAR-tree, that indexes the topic vectors of the places together with their spatial locations, in order to facilitate efficient group recommendation. We propose and compare three variants of the TAR-tree and a compression technique for the index, that improves its performance. The proposed techniques are evaluated on real data; the results demonstrate the efficiency and effectiveness of our methods.

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Notes

  1. 1.

    http://www.yelp.com/dataset_challenge.

  2. 2.

    http://www.yelp.com/dataset_challenge.

  3. 3.

    https://foursquare.com/about/.

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Acknowledgements

We thank the reviewers for their valuable comments. This work is partially supported by GRF Grants 17201414 and 17205015 from Hong Kong Research Grant Council. It has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 657347.

Author information

Authors and Affiliations

  1. The University of Hong Kong, Hong Kong, Hong Kong

    Yuqiu Qian, Ziyu Lu & David W. Cheung

  2. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

Authors
  1. Yuqiu Qian
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  2. Ziyu Lu
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  3. Nikos Mamoulis
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  4. David W. Cheung
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Corresponding author

Correspondence to Yuqiu Qian .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

    Michael Gertz

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. University of Queensland, Brisbane, Queensland, Australia

    Xiaofang Zhou

  4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

    Erik Hoel

  5. Auburn University, Auburn, Alabama, USA

    Wei-Shinn Ku

  6. Free University of Berlin, Dahlem, Berlin, Germany

    Agnes Voisard

  7. Microsoft, Redmond, Washington, USA

    Chengyang Zhang

  8. California State University, Sacramento, California, USA

    Haiquan Chen

  9. LinkedIn, Sunnyvale, California, USA

    Liang Tang

  10. University of North Texas, Denton, Texas, USA

    Yan Huang

  11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

  12. Oracle, Redwood Shores, California, USA

    Siva Ravada

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Qian, Y., Lu, Z., Mamoulis, N., Cheung, D.W. (2017). P-LAG: Location-Aware Group Recommendation for Passive Users. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-64367-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64366-3

  • Online ISBN: 978-3-319-64367-0

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