{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:33:11Z","timestamp":1762522391557},"reference-count":25,"publisher":"World Scientific Pub Co Pte Lt","issue":"11n12","funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61100043","61702144"],"award-info":[{"award-number":["61100043","61702144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Science and Technology Project of Zhejiang","award":["2017C01010"],"award-info":[{"award-number":["2017C01010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:p>By suggesting new visiting places, point-of-interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the data sparsity and complexity of user check-in behavior still pose big challenges to accurate personalized POI recommendation. To tackle these problems, in this paper, we propose a POI recommendation model named HeteGeoRankRec based on user contextual behavior semantics. First, we employ the meta-path of heterogeneous information network (HIN) to represent the complex semantic relationship among users and POIs. Second, we introduce different context constraints (such as time and weather) into the meta-path, to reveal the fine-grained user behavioral features. Afterwards, we propose a weighted matrix factorization model which considers the influence of geographical distance through the user\u2013POI semantic correlativity matrices generated by multiple meta-paths. Finally, we present a fusion method based on learning to rank, which unifies the recommendation results of different meta-paths as the final user preference. The experiments on the real data collected from Foursquare demonstrate that HeteGeoRankRec has the better performance than the state-of-the-art baselines.<\/jats:p>","DOI":"10.1142\/s0218194019400217","type":"journal-article","created":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T08:53:07Z","timestamp":1581497587000},"page":"1781-1799","source":"Crossref","is-referenced-by-count":8,"title":["Point-of-Interest Recommendation Based on User Contextual Behavior Semantics"],"prefix":"10.1142","volume":"29","author":[{"given":"Dongjin","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaihui","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, P. R. 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