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MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendation

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Web Information Systems Engineering – WISE 2021 (WISE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13081))

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Abstract

Next Point-of-Interest (POI) recommendation has become a vital research trend, helping people find interesting and attractive locations. Existing methods usually exploit the individual-level POI sequences but failed to utilize the information of collective-level POI sequences. Since collective-level POIs, like shopping malls or plazas, are common in the real world, we argue that only the individual-level POI sequences cannot represent more semantic features and cannot express complete transition patterns. To this end, we propose a novel Multi-Granularity Self-Attention Network (MGSAN) for next POI recommendation, which utilizes the multi-granularity representation and the self-attention mechanism to capture the transition patterns of individual-level and collective-level POI sequences on two different levels of granularities. Specifically, individual-level and collective-level POI sequences are first constructed and embeddings of each check-in tuple are normalized. Then, MGSAN incorporates spatio-temporal features by introducing two temporal-aware encoders and two spatial-aware encoders and learns sequential patterns with the self-attention network for two granularities. Finally, we recommended a user’s next POI with the help of two sub-tasks, i.e., the activity task to predict the next category and the auxiliary task to predict the next POI type. Extensive experiments on three real-world datasets show that MGSAN outperforms state-of-the-art methods consistently.

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Notes

  1. 1.

    https://sites.google.com/site/yangdingqi/home/foursquare-dataset.

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Acknowledgements

This research was partially supported by NSFC (No. 61876117, 61876217, 61872258, 61728205), Open Program of Key Lab of IIP of CAS (No. IIP2019-1) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Correspondence to Xuefeng Xian or Pengpeng Zhao .

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Li, Y., Xian, X., Zhao, P., Liu, Y., Sheng, V.S. (2021). MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendation. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13081. Springer, Cham. https://doi.org/10.1007/978-3-030-91560-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-91560-5_14

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