research-article

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

Authors:

Yong Liang Goh,

Jagannadan VaradarajanAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 845 - 854

https://doi.org/10.1145/3340531.3411876

Published: 19 October 2020 Publication History

Abstract

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods.

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Cited By

Liu JGao HYang CShi CYang TCheng HXie QWang XWang D(2025)Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest RecommendationTsinghua Science and Technology10.26599/TST.2023.901014830:1(186-197)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2023.9010148
Qi LLiu YLiu WPei SXu XZhang XWang YDou WLarson K(2024)Counterfactual user sequence synthesis augmented with continuous time dynamic preference modeling for sequential POI recommendationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/255(2306-2314)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/255
Takagi SXiong LKato FCao YYoshikawa M(2024)HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data SynthesizationProceedings of the VLDB Endowment10.14778/3681954.368198317:11(3058-3071)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681983
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Index Terms

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Publication History

Published: 19 October 2020

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October 19 - 23, 2020

Virtual Event, Ireland

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Liu JGao HYang CShi CYang TCheng HXie QWang XWang D(2025)Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest RecommendationTsinghua Science and Technology10.26599/TST.2023.901014830:1(186-197)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2023.9010148
Qi LLiu YLiu WPei SXu XZhang XWang YDou WLarson K(2024)Counterfactual user sequence synthesis augmented with continuous time dynamic preference modeling for sequential POI recommendationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/255(2306-2314)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/255
Takagi SXiong LKato FCao YYoshikawa M(2024)HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data SynthesizationProceedings of the VLDB Endowment10.14778/3681954.368198317:11(3058-3071)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681983
Sun KLi CQian T(2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664284
Lee WLauw H(2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3663474
Li Pde Rijke MXue HAo SSong YSalim FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Models for Next Point-of-Interest RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657840(1463-1472)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657840
Zhang JMa MGao XChen G(2024)Encoder-Decoder Based Route Generation Model for Flexible Travel RecommendationIEEE Transactions on Services Computing10.1109/TSC.2024.337623117:3(905-920)Online publication date: May-2024
https://doi.org/10.1109/TSC.2024.3376231
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
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility Via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317175(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3317175
Kong XChen ZLi JBi JShen G(2024)KGNext: Knowledge-Graph-Enhanced Transformer for Next POI Recommendation With Uncertain Check-InsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339650611:5(6637-6648)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3396506
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