{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T05:22:15Z","timestamp":1765948935109,"version":"3.48.0"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"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":["62576079"],"award-info":[{"award-number":["62576079"]}],"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":["62076054"],"award-info":[{"award-number":["62076054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Point-of-Interest (POI) recommendation predicts users\u2019 future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorization model user\u2013POI interaction matrices fail to fully leverage spatio-temporal information and semantic attributes, leading to weak performance on sparse and long-tail POIs. Recently, Graph Neural Networks (GNNs) have been applied by constructing heterogeneous user\u2013POI graphs to capture high-order relations. However, they still struggle to effectively integrate spatio-temporal and semantic information and enhance the discriminative power of learned representations. To overcome these issues, we propose Spatio-Temporal and Semantic Dual-Channel Contrastive Alignment for POI Recommendation (S2DCRec), a novel framework integrating spatio-temporal and semantic information. It employs hierarchical relational encoding to capture fine-grained behavioral patterns and high-level semantic dependencies. The model jointly captures user\u2013POI interactions, temporal dynamics, and semantic correlations in a unified framework. Furthermore, our alignment strategy ensures micro-level collaborative and spatio-temporal consistency and macro-level semantic coherence, enabling fine-grained embedding fusion and interpretable contrastive learning. Experiments on real-world datasets, Foursquare NYC, and Yelp, show that S2DCRec outperforms all baselines, improving F1 scores by 4.04% and 3.01%, respectively. These results demonstrate the effectiveness of the dual-channel design in capturing both sequential and semantic dependencies for accurate POI recommendation.<\/jats:p>","DOI":"10.3390\/bdcc9120322","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T15:15:08Z","timestamp":1765811708000},"page":"322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatio-Temporal and Semantic Dual-Channel Contrastive Alignment for POI Recommendation"],"prefix":"10.3390","volume":"9","author":[{"given":"Chong","family":"Bu","sequence":"first","affiliation":[{"name":"School of Computer Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yujie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jing","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Network and Communication Engineering, Chengdu Technological University, Chengdu 611730, China"}]},{"given":"Manqi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Maoyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jiarui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. 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