POI Recommendation for Occasional Groups Based on Hybrid Graph Neural Networks

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aut.filerelease.date2025-09-19
aut.relation.articlenumber121583
aut.relation.endpage121583
aut.relation.journalExpert Systems with Applications
aut.relation.startpage121583
aut.relation.volume237
dc.contributor.authorMeng, L
dc.contributor.authorLiu, Z
dc.contributor.authorChu, D
dc.contributor.authorSheng, QZ
dc.contributor.authorYu, J
dc.contributor.authorSong, X
dc.date.accessioned2023-10-12T21:29:30Z
dc.date.available2023-10-12T21:29:30Z
dc.date.issued2023-09-19
dc.description.abstractRecently, POI (Point-of-interest) recommendation for groups has become a critical challenge when helping groups to discover potentially interesting new places, and some effective recommendation models have been proposed to address this issue. However, most existing research focuses on POI recommendation for fixed groups, few studies have been conducted on POI recommendation for occasional groups. To tackle this issue, we propose a POI recommendation model for occasional groups based on Hybrid Graph Neural Networks (termed as PROG-HGNN) which combines excellent graph neural networks models. Firstly, PROG-HGNN generates the fitted representation of the occasional group based on the Node Influence Indicator (INF) method and Graph Attention Networks (GAT) model. Then, PROG-HGNN learns POIs’ representations containing members’ POI interaction preferences and members’ POI transfer preferences with the Signed Bipartite Graph Neural Networks (SBGNN) model and the Session-based Graph Neural Networks (SRGNN) model, respectively. Finally, PROG-HGNN recommends the potential POIs for the occasional group based on the fitted representation of the occasional group and the learned representations of POIs. We verify our proposed model on three public benchmark datasets (Foursquare, Gowalla and Yelp), which contains 124,933 to 860,888 POI check-in records. The comparison between our proposed model and the twelve baseline models demonstrates the outstanding performance of PROG-HGNN. In terms of Precision@K and Recall@K, our model achieves about 32.92% and 19.67% improvement compared with the best baseline models on the three benchmark datasets averagely. Adequate ablation experiments prove the effectiveness of the members’ POI interaction preferences learning module and POI transfer preferences learning module.
dc.identifier.citationExpert Systems with Applications, ISSN: 0957-4174 (Print), Elsevier BV, 237, 121583-121583. doi: 10.1016/j.eswa.2023.121583
dc.identifier.doi10.1016/j.eswa.2023.121583
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10292/16765
dc.languageen
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0957417423020857
dc.rightsCopyright © 2023 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.rights.accessrightsOpenAccess
dc.subject46 Information and Computing Sciences
dc.subject4611 Machine Learning
dc.subjectBehavioral and Social Science
dc.subject01 Mathematical Sciences
dc.subject08 Information and Computing Sciences
dc.subject09 Engineering
dc.subjectArtificial Intelligence & Image Processing
dc.titlePOI Recommendation for Occasional Groups Based on Hybrid Graph Neural Networks
dc.typeJournal Article
pubs.elements-id524595
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