Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation
With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising re...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4979 |
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| author | Qiuhan Han Atsushi Yoshikawa Masayuki Yamamura |
| author_facet | Qiuhan Han Atsushi Yoshikawa Masayuki Yamamura |
| author_sort | Qiuhan Han |
| collection | DOAJ |
| description | With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising results in this field, they typically construct single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. To address this limitation, we propose a novel Hierarchical Graph Learning (HGL) framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns. To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Compared to traditional models, our hierarchical structure improves cold-start robustness and achieves superior performance on real-world datasets. While the incorporation of multi-layer attention and clustering introduces moderate computational overhead, the cost remains acceptable for offline recommendation contexts. |
| format | Article |
| id | doaj-art-a7b6c9a412cc4b0d8f73b49e36c6c967 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a7b6c9a412cc4b0d8f73b49e36c6c9672025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-04-01159497910.3390/app15094979Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest RecommendationQiuhan Han0Atsushi Yoshikawa1Masayuki Yamamura2Department of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo 152-8550, JapanSchool of Engineering, Kanto Gakuin University, Yokohama 236-0037, JapanDepartment of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo 152-8550, JapanWith the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising results in this field, they typically construct single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. To address this limitation, we propose a novel Hierarchical Graph Learning (HGL) framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns. To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Compared to traditional models, our hierarchical structure improves cold-start robustness and achieves superior performance on real-world datasets. While the incorporation of multi-layer attention and clustering introduces moderate computational overhead, the cost remains acceptable for offline recommendation contexts.https://www.mdpi.com/2076-3417/15/9/4979next POI recommendationgraph neural networksspatio-temporal data mining |
| spellingShingle | Qiuhan Han Atsushi Yoshikawa Masayuki Yamamura Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation Applied Sciences next POI recommendation graph neural networks spatio-temporal data mining |
| title | Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation |
| title_full | Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation |
| title_fullStr | Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation |
| title_full_unstemmed | Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation |
| title_short | Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation |
| title_sort | hierarchical graph learning with cross layer information propagation for next point of interest recommendation |
| topic | next POI recommendation graph neural networks spatio-temporal data mining |
| url | https://www.mdpi.com/2076-3417/15/9/4979 |
| work_keys_str_mv | AT qiuhanhan hierarchicalgraphlearningwithcrosslayerinformationpropagationfornextpointofinterestrecommendation AT atsushiyoshikawa hierarchicalgraphlearningwithcrosslayerinformationpropagationfornextpointofinterestrecommendation AT masayukiyamamura hierarchicalgraphlearningwithcrosslayerinformationpropagationfornextpointofinterestrecommendation |