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Open Access

Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest Recommendation

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Meituan, Beijing 100102, China
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Abstract

As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks (GNNs) have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reasons. Self-supervised learning gives a new idea to alleviate the data sparsity problem, but most existing self-supervised recommendation methods cannot be directly used in the spatio-temporal graph of POI recommendations. In this paper, we propose a novel heterogeneous spatio-temporal graph contrastive learning method, HestGCL, to compensate for existing GNN-based methods’ shortcomings. To model spatio-temporal information, we generate spatio-temporally specific views and design view-specific heterogeneous graph neural networks to model spatial and temporal information, respectively. To alleviate data sparsity, we propose a cross-view contrastive strategy to capture differences and correlations among views, providing more supervision signals and boosting the overall performance collaboratively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HestGCL, which significantly outperforms existing methods.

Keywords

point-of-interest recommendationgraph neural networkself-supervised learning

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Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 186-197
DOI: 10.26599/TST.2023.9010148
Cite this article:
Liu J, Gao H, Yang C, et al. Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest Recommendation. Tsinghua Science and Technology, 2025, 30(1): 186-197. https://doi.org/10.26599/TST.2023.9010148

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Received: 21 August 2023
Revised: 14 November 2023
Accepted: 06 December 2023
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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