{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:45:05Z","timestamp":1761597905017,"version":"3.41.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T00:00:00Z","timestamp":1517788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2018,8,31]]},"abstract":"<jats:p>\n            Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi-dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users\u2019 check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more\n            <jats:italic>compact<\/jats:italic>\n            model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.\n          <\/jats:p>","DOI":"10.1145\/3134438","type":"journal-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T18:13:28Z","timestamp":1517940808000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4149-839X","authenticated-orcid":false,"given":"Lina","family":"Yao","sequence":"first","affiliation":[{"name":"University of New South Wales"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Macquarie University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9582-3445","authenticated-orcid":false,"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"University of New South Wales"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei Emma","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macquarie University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongrui","family":"Qin","sequence":"additional","affiliation":[{"name":"The University of Huddersfield"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,2,5]]},"reference":[{"volume-title":"Recommender Systems Handbook","author":"Adomavicius Gediminas","key":"e_1_2_1_1_1","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin . 2011. Context-aware recommender systems . In Recommender Systems Handbook . Springer , 217--253. Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-aware recommender systems. In Recommender Systems Handbook. Springer, 217--253."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424348"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989656.1989660"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/06-BA104"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence.","author":"Chen Xuefeng","year":"2015","unstructured":"Xuefeng Chen , Yifeng Zeng , Gao Cong , Shengchao Qin , Yanping Xiang , and Yuanshun Dai . 2015 . On information coverage for location category based point-of-interest recommendation . In Proceedings of the 29th AAAI Conference on Artificial Intelligence. Xuefeng Chen, Yifeng Zeng, Gao Cong, Shengchao Qin, Yanping Xiang, and Yuanshun Dai. 2015. On information coverage for location category based point-of-interest recommendation. In Proceedings of the 29th AAAI Conference on Artificial Intelligence."},{"volume-title":"Proceedings of the 26th AAAI Conference on Artificial Intelligence.","author":"Cheng Chen","key":"e_1_2_1_6_1","unstructured":"Chen Cheng , Haiqin Yang , Irwin King , and Michael R. Lyu . 2012. Fused matrix factorization with geographical and social influence in location-based social networks . In Proceedings of the 26th AAAI Conference on Artificial Intelligence. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of the 26th AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 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