Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation

Buru Chang, Gwanghoon Jang, Seoyoon Kim, Jaewoo Kang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    Several geographical latent representation models that capture geographical influences among points-of-interest (POIs) have been proposed. Although the models improve POI recommendation performance, they depend on shallow methods that cannot effectively capture highly non-linear geographical influences from complex user-POI networks. In this paper, we propose a new graph-based geographical latent representation model (GGLR) which can capture highly non-linear geographical influences from complex user-POI networks. Our proposed GGLR considers two types of geographical influences: ingoing influences and outgoing influences. Based on a graph auto-encoder, geographical latent representations of ingoing and outgoing influences are trained to increase geographical influences between two consecutive POIs that frequently appear in check-in histories. Furthermore, we propose a graph neural network-based POI recommendation model (GPR) that uses the trained geographical latent representations of ingoing and outgoing influences for the estimation of user preferences. In the experimental evaluation on real-world datasets, we show that GGLR effectively captures highly non-linear geographical influences and GPR achieves state-of-the-art performance.

    Original languageEnglish
    Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
    PublisherAssociation for Computing Machinery
    Pages135-144
    Number of pages10
    ISBN (Electronic)9781450368599
    DOIs
    Publication statusPublished - 2020 Oct 19
    Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
    Duration: 2020 Oct 192020 Oct 23

    Publication series

    NameInternational Conference on Information and Knowledge Management, Proceedings

    Conference

    Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
    Country/TerritoryIreland
    CityVirtual, Online
    Period20/10/1920/10/23

    Keywords

    • POI recommendation
    • collaborative filtering
    • location-based social network
    • point-of-interest
    • recommender system

    ASJC Scopus subject areas

    • Business, Management and Accounting(all)
    • Decision Sciences(all)

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