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  • Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

    Author(s)
    Sun, Ke
    Qian, Tieyun
    Chen, Tong
    Liang, Yile
    Nguyen, Quoc Viet Hung
    Yin, Hongzhi
    Griffith University Author(s)
    Nguyen, Henry
    Year published
    2020
    Metadata
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    Abstract
    Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, ...
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    Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.
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    Conference Title
    Proceedings of the AAAI Conference on Artificial Intelligence
    Volume
    34
    Issue
    01
    DOI
    https://doi.org/10.1609/aaai.v34i01.5353
    Subject
    Pattern recognition
    Data mining and knowledge discovery
    Publication URI
    http://hdl.handle.net/10072/399266
    Collection
    • Conference outputs

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