{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:40:55Z","timestamp":1780609255473,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["42471465"],"award-info":[{"award-number":["42471465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["41871321"],"award-info":[{"award-number":["41871321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3005702"],"award-info":[{"award-number":["2022YFC3005702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Existing point-of-interest (POI) recommendation methods often fail to capture complex contextual dependencies and suffer from severe data sparsity in location-based social networks (LBSNs). To address these limitations, this study proposes a Context-Aware Knowledge Graph Learning (CKGL) method that integrates multi-dimensional semantic information, spatio-temporal dependencies, and social relationships into a unified knowledge graph framework. First, the Context-Aware Knowledge Graph Construction (CKGC) module builds a unified POI knowledge graph that captures heterogeneous relationships among users, POIs, regions of interest (ROIs), and social links. Then, the Context-Aware Knowledge Graph Embedding (CKGE) module, based on the Translational Distance Model with Relation-Specific Spaces (TransR), learns relation-specific embeddings of entities to preserve heterogeneous semantics. Next, a Spatio-Temporal Gated Graph Neural Network (STG-GNN) captures temporal dynamics and spatial dependencies in user check-in behaviors, while the Relation-Aware Graph Attention Network (RA-GAT) enhances multi-relational reasoning and information aggregation across heterogeneous relations. Extensive experiments on two real-world LBSN datasets, Gowalla and Brightkite, demonstrate that CKGL significantly outperforms several baseline models on Recall and Normalized Discounted Cumulative Gain (NDCG), validating its effectiveness in capturing contextual semantics and improving recommendation accuracy under sparse and complex scenarios.<\/jats:p>","DOI":"10.3390\/ijgi15010014","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:08:11Z","timestamp":1767190091000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Context-Aware Knowledge Graph Learning for Point-of-Interest Recommendation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3381-357X","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1964-0614","authenticated-orcid":false,"given":"Di","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Han","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3991\/ijim.v19i03.49427","article-title":"Knowledge graph-based recommender systems to mitigate data sparsity: A systematic literature review","volume":"19","author":"Boualaoui","year":"2025","journal-title":"Int. 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