{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T05:55:01Z","timestamp":1773554101783,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015956","name":"Key Research and Development Program of Guangdong Province","doi-asserted-by":"crossref","award":["2020B0101130009"],"award-info":[{"award-number":["2020B0101130009"]}],"id":[{"id":"10.13039\/501100015956","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Science and Technology Planning Project of Guangdong Province","award":["2015B010104003"],"award-info":[{"award-number":["2015B010104003"]}]},{"name":"Key Science and Technology Planning Projects of Guangzhou","award":["201604046007"],"award-info":[{"award-number":["201604046007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Next Point-of-Interest (POI) recommendations are pivotal for enhancing location-based services; however, accurate prediction remains challenging due to the complex interplay between dynamic user preferences and spatiotemporal constraints. Existing graph-sequence hybrids often fail to unify these dimensions, typically treating temporal contexts as disjoint features or neglecting implicit collaborative signals within sparse user trajectories. This fragmentation limits the ability to capture high-order dependencies in user mobility. To address these challenges, we propose UPTRec, a unified framework that synergizes social, spatial, and temporal reasoning. UPTRec constructs a TF-IDF-weighted user similarity graph to recover latent social connections and a flow-based POI-transition graph to encode sequential mobility patterns. These structural priors are fused with fine-grained temporal-category embeddings (utilizing Time2Vec and periodic encoding) via a multi-layer Transformer encoder to comprehensively capture user behavior. Extensive experiments on three real-world datasets (NYC, TKY, and CA) demonstrate that UPTRec achieves state-of-the-art performance among the compared baselines under the same experimental settings. On the NYC dataset, UPTRec yields a Top-1 Accuracy of 25.76% and a Mean Reciprocal Rank (MRR) of 0.3879, representing a relative improvement of 5.8% and 7.1% over the strongest baseline (GETNext). These results validate the efficacy of jointly modeling collaborative and spatiotemporal dependencies.<\/jats:p>","DOI":"10.3390\/ijgi15030122","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:26:38Z","timestamp":1773393998000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UPTRec: Fusing User Graph, Point-of-Interest Transitions, and Temporal Embeddings for Next Point-of-Interest Recommendations"],"prefix":"10.3390","volume":"15","author":[{"given":"Junxia","family":"Li","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linyuan","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4394-6934","authenticated-orcid":false,"given":"Yuezhen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6091-3114","authenticated-orcid":false,"given":"Qianxia","family":"Li","sequence":"additional","affiliation":[{"name":"Surveying and Mapping Institute, Lands and Resource Department of Guangdong Province, Guangzhou 510500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, G., Chen, Y., Lu, Y., Wang, X., and Feng, S. 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