Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation

Wang, X; Sun, G; Fang, X; Yang, J; Wang, S

Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation

Wang, X Sun, G Fang, X Yang, J Wang, S

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Publisher:: International Joint Conferences on Artificial Intelligence Organization
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022, pp. 3530-3536
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Sun, G
dc.contributor.author	Fang, X
dc.contributor.author	Yang, J
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.date	2022-07-23
dc.date.accessioned	2022-09-06T04:38:09Z
dc.date.available	2022-09-06T04:38:09Z
dc.date.issued	2022-07-01
dc.identifier.citation	Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022, pp. 3530-3536
dc.identifier.uri	http://hdl.handle.net/10453/161429
dc.description.abstract	<jats:p>Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.</jats:p>
dc.language	en
dc.publisher	International Joint Conferences on Artificial Intelligence Organization
dc.relation.ispartof	Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
dc.relation.ispartof	Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
dc.relation.isbasedon	10.24963/ijcai.2022/490
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-06T04:38:08Z
pubs.finish-date	2022-07-29
pubs.publication-status	Published
pubs.start-date	2022-07-23

Abstract:

Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.

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http://hdl.handle.net/10453/161429