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Syst."],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>\n            The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user\u2019s decision-making for choosing a POI to visit. In this article, we focus on the\n            <jats:italic>spatiotemporal context-aware<\/jats:italic>\n            POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a\n            <jats:italic>spatiotemporal context-aware<\/jats:italic>\n            and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a \u201ctransition space\u201d where spatiotemporal contexts (i.e., a &lt;\n            <jats:italic>time, location<\/jats:italic>\n            &gt; pair) are modeled as\n            <jats:italic>translation vectors<\/jats:italic>\n            operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.\n          <\/jats:p>","DOI":"10.1145\/3295499","type":"journal-article","created":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T13:28:51Z","timestamp":1548682131000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":143,"title":["Spatiotemporal Representation Learning for Translation-Based POI Recommendation"],"prefix":"10.1145","volume":"37","author":[{"given":"Tieyun","family":"Qian","sequence":"first","affiliation":[{"name":"Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bei","family":"Liu","sequence":"additional","affiliation":[{"name":"Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quoc Viet Hung","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Griffith University, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"The University of Queensland, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,1,27]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Context-aware recommender systems. 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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 AAAI Conference on Artificial Intelligence. 37--43."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of AAAI Conference on Artificial Intelligence. 17--23","author":"Cheng Chen","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 AAAI Conference on Artificial Intelligence. 17--23 . Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. 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