research-article

Hierarchical Transformer with Spatio-temporal Context Aggregation for Next Point-of-interest Recommendation

Authors:

Zhenzhong ChenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 2

Article No.: 37, Pages 1 - 30

https://doi.org/10.1145/3597930

Published: 27 September 2023 Publication History

Abstract

Next point-of-interest (POI) recommendation is a critical task in location-based social networks, yet remains challenging due to a high degree of variation and personalization exhibited in user movements. In this work, we explore the latent hierarchical structure composed of multi-granularity short-term structural patterns in user check-in sequences. We propose a Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next POI recommendation, which employs stacked hierarchical encoders to recursively encode the spatio-temporal context and explicitly locate subsequences of different granularities. More specifically, in each encoder, the global attention layer captures the spatio-temporal context of the sequence, while the local attention layer performed within each subsequence enhances subsequence modeling using the local context. The sequence partition layer infers positions and lengths of subsequences from the global context adaptively, such that semantics in subsequences can be well preserved. Finally, the subsequence aggregation layer fuses representations within each subsequence to form the corresponding subsequence representation, thereby generating a new sequence of higher-level granularity. The stacking of hierarchical encoders captures the latent hierarchical structure of the check-in sequence, which is used to predict the next visiting POI. Extensive experiments on three public datasets demonstrate that the proposed model achieves superior performance while providing explanations for recommendations.

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Index Terms

Hierarchical Transformer with Spatio-temporal Context Aggregation for Next Point-of-interest Recommendation
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 2

March 2024

897 pages

EISSN:1558-2868

DOI:10.1145/3618075

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 27 September 2023

Online AM: 04 August 2023

Accepted: 18 May 2023

Revised: 04 March 2023

Received: 30 August 2022

Published in TOIS Volume 42, Issue 2

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Cited By

Liang XYang EDeng CYang Y(2024)CrossFormer: Cross-modal Representation Learning via Heterogeneous Graph TransformerACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3688801Online publication date: 20-Sep-2024
https://doi.org/10.1145/3688801
An JGao MTang J(2024)MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI RecommendationACM Transactions on Information Systems10.1145/366465142:6(1-29)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664651
Wang LWu SLiu QZhu YTao XZhang MWang L(2024)Bi-Level Graph Structure Learning for Next POI RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339768336:11(5695-5708)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3397683
Rezaei ESeyedmohammadi SAbouei JPlataniotis K(2024)FedDist-POIRec: Federated Distillation for Point-Of-Interest Recommendation in Human Mobility Prediction2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS59971.2024.10555827(1-6)Online publication date: 15-May-2024
https://doi.org/10.1109/ICHMS59971.2024.10555827
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