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Disentangled Contrastive Hypergraph Learning for Next POI Recommendation

Published: 11 July 2024 Publication History

Abstract

Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Most existing sequential-based and graph neural network-based methods have explored various approaches to modeling user visiting behaviors and have achieved considerable performances. However, two key issues have received less attention: i) Most previous studies have ignored the fact that user preferences are diverse and constantly changing in terms of various aspects, leading to entangled and suboptimal user representations. ii) Many existing methods have inadequately modeled the crucial cooperative associations between different aspects, hindering the ability to capture complementary recommendation effects during the learning process. To tackle these challenges, we propose a novel framework <u>D</u>isentangled <u>C</u>ontrastive <u>H</u>ypergraph <u>L</u>earning (DCHL) for next POI recommendation. Specifically, we design a multi-view disentangled hypergraph learning component to disentangle intrinsic aspects among collaborative, transitional and geographical views with adjusted hypergraph convolutional networks. Additionally, we propose an adaptive fusion method to integrate multi-view information automatically. Finally, cross-view contrastive learning is employed to capture cooperative associations among views and reinforce the quality of user and POI representations based on self-discrimination. Extensive experiments on three real-world datasets validate the superiority of our proposal over various state-of-the-arts. To facilitate future research, our code is available at https://github.com/icmpnorequest/SIGIR2024_DCHL.

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Cited By

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  • (2024)Interpretable Embeddings for Next Point-of-Interest Recommendation via Large Language Model Question–AnsweringMathematics10.3390/math1222359212:22(3592)Online publication date: 16-Nov-2024
  • (2024)Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation MethodElectronics10.3390/electronics1324495413:24(4954)Online publication date: 16-Dec-2024
  • (2024)Adaptive Graph-Based Uncertain Trajectory Data Augmentation Network for Next POI Recommendation2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831939(4724-4729)Online publication date: 6-Oct-2024
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    cover image ACM Conferences
    SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2024
    3164 pages
    ISBN:9798400704314
    DOI:10.1145/3626772
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    Published: 11 July 2024

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    Author Tags

    1. contrastive learning
    2. disentangled representation
    3. hypergraph neural networks
    4. next poi recommendation

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    View all
    • (2024)Interpretable Embeddings for Next Point-of-Interest Recommendation via Large Language Model Question–AnsweringMathematics10.3390/math1222359212:22(3592)Online publication date: 16-Nov-2024
    • (2024)Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation MethodElectronics10.3390/electronics1324495413:24(4954)Online publication date: 16-Dec-2024
    • (2024)Adaptive Graph-Based Uncertain Trajectory Data Augmentation Network for Next POI Recommendation2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831939(4724-4729)Online publication date: 6-Oct-2024
    • (2024)Hypergraph contrastive learning for recommendation with side informationInternational Journal of Intelligent Computing and Cybernetics10.1108/IJICC-06-2024-026617:4(657-670)Online publication date: 27-Sep-2024
    • (2024)Novel directed hypergraph p-Laplacian based semi-supervised learning method: theory and algorithmsInternational Journal of Information Technology10.1007/s41870-024-02264-4Online publication date: 11-Dec-2024
    • (2024)DyAGL: A Dynamic-Aware Adaptive Graph Learning Network for Next POI RecommendationPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0116-5_30(361-374)Online publication date: 12-Nov-2024

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