{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T15:41:24Z","timestamp":1769701284958,"version":"3.49.0"},"reference-count":14,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:p>This paper focuses on the task of Point-of-interest (POI) recommendation whose goal is to generate a list of POIs for a target user based on his or her history check-in records. Different from the traditional recommendation tasks (e.g., movie recommendation), there are many factors, like temporal factor and geographical factor, which make a great influence on user preference. Though existing POI recommendation methods tend to model the user preference from temporal factor, geographical factor or social factor, they fail to model these factors into a jointly model, leading to learn the suboptimal user preference. To tackle this issue, we propose a Muti-channel Graph Attention Network (MGAN) for POI recommendation which learns the user preference from multiple aspects in a unify model. Specifically, MGAN first constructs several graphs with corresponding contextual features to capture the user preference from temporal, geographical, semantic and social aspects. Then MGAN leverages the graph attention networks to learn the representations of POIs from these graphs. Finally, MGAN estimates the user preference from the history check-in records and other similar users via the learned POI representations. We conduct extensive experiments on real-world datasets. And the results indicate that our proposed MGAN outperforms mainstream POI recommendation methods.<\/jats:p>","DOI":"10.3233\/jifs-222952","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T10:48:46Z","timestamp":1677840526000},"page":"8375-8385","source":"Crossref","is-referenced-by-count":2,"title":["Muti-channel graph attention networks for POI recommendation"],"prefix":"10.1177","volume":"44","author":[{"given":"Yisheng","family":"Wu","sequence":"first","affiliation":[{"name":"Faculty of Humanities, Zhaoqing Medical College, Zhaoqing, China"}]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information Technology, Anqing Vocational & Technical College, Anqing, China"}]},{"given":"Haiping","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Humanities, Zhaoqing Medical College, Zhaoqing, China"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/JIFS-222952_ref1","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/TKDE.2014.2362525","article-title":"A general geographical probabilistic factor model for point of interest recommendation[J]","volume":"27","author":"Liu","year":"2014","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-222952_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2020.06.099","article-title":"A user-based aggregation topic model for understanding user\u2019s preference and intention in social network[J]","volume":"413","author":"Shi","year":"2020","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-222952_ref9","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neucom.2017.02.067","article-title":"A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS[J]","volume":"242","author":"Ying","year":"2017","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-222952_ref10","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.knosys.2017.04.013","article-title":"CTF-ARA: An adaptive method for POI recommendation based on check-in and temporal features[J]","volume":"128","author":"Si","year":"2017","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/JIFS-222952_ref19","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2019.12.122","article-title":"Exploiting geographical-temporal awareness attention for next point-of-interest recommendation[J]","volume":"400","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-222952_ref22","first-page":"1837","article-title":"Category-aware next point-of-interest recommendation via listwise bayesian personalized ranking[C]\/\/","volume":"17","author":"He","year":"2017","journal-title":"IJCAI"},{"key":"10.3233\/JIFS-222952_ref24","first-page":"1264","article-title":"A category-aware deep model for successive POI recommendation on sparse check-in data[C]\/\/","volume":"2020","author":"Yu","year":"2020","journal-title":"Proceedings of the Web Conference"},{"key":"10.3233\/JIFS-222952_ref25","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ins.2019.12.006","article-title":"Modeling hierarchical category transition for next POI recommendation with uncertain check-ins[J]","volume":"515","author":"Zhang","year":"2020","journal-title":"Information Sciences"},{"issue":"4","key":"10.3233\/JIFS-222952_ref27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485631","article-title":"STARec: Adaptive Learning with Spatiotemporal and Activity Influence for POI Recommendation[J]","volume":"40","author":"Ji","year":"2021","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"3","key":"10.3233\/JIFS-222952_ref29","first-page":"61","article-title":"CARec: Content-Aware Point-of-Interest Recommendation via Adaptive Bayesian Personalized Ranking[J]","volume":"15","author":"Liu","year":"2019","journal-title":"Aust. 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Syst."},{"issue":"3","key":"10.3233\/JIFS-222952_ref30","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1007\/s10489-018-1276-1","article-title":"Content-aware point-of-interest recommendation based on convolutional neural network[J]","volume":"49","author":"Xing","year":"2019","journal-title":"Applied Intelligence"},{"issue":"3","key":"10.3233\/JIFS-222952_ref31","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11042-016-4209-1","article-title":"A holistic approach for personalization, relevance feedback & recommendation in enriched multimedia content[J],","volume":"77","author":"Stai","year":"2018","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/JIFS-222952_ref32","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2022.02.070","article-title":"FG-CF: Friends-aware graph collaborative filtering for POI recommendation[J]","volume":"488","author":"Cai","year":"2022","journal-title":"Neurocomputing"},{"issue":"5","key":"10.3233\/JIFS-222952_ref35","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1007\/s11280-021-00895-2","article-title":"HOPE: a hybrid deep neural model for out-of-town next POI recommendation[J]","volume":"24","author":"Sun","year":"2021","journal-title":"World Wide Web"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-222952","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T05:53:59Z","timestamp":1769666039000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-222952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":14,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-222952","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,4]]}}}