A Survey on Graph Neural Network-Based Next POI Recommendation for Smart Cities

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aut.relation.endpage20
aut.relation.journalJournal of Reliable Intelligent Environments
aut.relation.startpage1
dc.contributor.authorYu, J
dc.contributor.authorGuo, L
dc.contributor.authorZhang, J
dc.contributor.authorWang, G
dc.date.accessioned2024-08-14T03:07:17Z
dc.date.available2024-08-14T03:07:17Z
dc.date.issued2024-07-26
dc.description.abstractAmid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research.
dc.identifier.citationJournal of Reliable Intelligent Environments, ISSN: 2199-4668 (Print); 2199-4676 (Online), Springer Science and Business Media LLC, 1-20. doi: 10.1007/s40860-024-00233-z
dc.identifier.doi10.1007/s40860-024-00233-z
dc.identifier.issn2199-4668
dc.identifier.issn2199-4676
dc.identifier.urihttp://hdl.handle.net/10292/17889
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttps://link.springer.com/article/10.1007/s40860-024-00233-z
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subjectClinical Research
dc.titleA Survey on Graph Neural Network-Based Next POI Recommendation for Smart Cities
dc.typeJournal Article
pubs.elements-id564724
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