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
Rao XJiang RShang SChen LHan PYao BKalnis P(2025)Next Point-of-Interest Recommendation With Adaptive Graph Contrastive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350948037:3(1366-1379)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3509480
Tang YHe JZhao Z(2025)Activity-Aware Human Mobility Prediction With Hierarchical Graph Attention Recurrent NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351369526:2(1604-1616)Online publication date: Feb-2025
https://doi.org/10.1109/TITS.2024.3513695
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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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Economic Development Board of Singapore

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

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
Rao XJiang RShang SChen LHan PYao BKalnis P(2025)Next Point-of-Interest Recommendation With Adaptive Graph Contrastive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350948037:3(1366-1379)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3509480
Tang YHe JZhao Z(2025)Activity-Aware Human Mobility Prediction With Hierarchical Graph Attention Recurrent NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351369526:2(1604-1616)Online publication date: Feb-2025
https://doi.org/10.1109/TITS.2024.3513695
Zhang YWang BZhang QZhu SMa Y(2025)Hypergraph User Embeddings and Session Contrastive Learning for POI RecommendationIEEE Access10.1109/ACCESS.2025.353139413(17983-17995)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3531394
Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Yang KLi GWang LXie J(2025)A framework for solving bias in graph-based recommender systems with a causal perspectiveApplied Intelligence10.1007/s10489-025-06388-355:6Online publication date: 25-Feb-2025
https://doi.org/10.1007/s10489-025-06388-3
Sang CYang YZhang YLiao S(2025)A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendationApplied Intelligence10.1007/s10489-025-06290-y55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06290-y
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
Zhang JZhu GChen YLu YWang XFeng S(2024)Multi-view Contrastive Learning for Next POI RecommendationProceedings of the 2024 8th International Conference on Computing and Data Analysis10.1145/3705374.3705385(75-80)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3705374.3705385
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