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Data Sci."],"published-print":{"date-parts":[[2020,2,29]]},"abstract":"<jats:p>\n                    With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to incorporate multiple factors associated with both POIs and users due to the heterogeneity and implicity of this information. To alleviate the problem, this work proposes a novel group-based method for POI recommendation jointly considering the reviews, categories, and geographical locations, called the\n                    <jats:italic toggle=\"yes\">Group-based Temporal Sentiment-Aspect-Region Recurrent Neural Network<\/jats:italic>\n                    (GTSAR-RNN). We divide the users into different groups and then train an individual RNN for each group with the goal of improving its pertinence. In GTSAR-RNN, we consider not only the effects of temporal and geographical contexts but also the users\u2019 sentimental opinions on locations. Experimental results show that GTSAR-RNN acquires significant improvements over the baseline methods on real datasets.\n                  <\/jats:p>","DOI":"10.1145\/3343037","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T11:04:32Z","timestamp":1584011072000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Group-Based Recurrent Neural Networks for POI Recommendation"],"prefix":"10.1145","volume":"1","author":[{"given":"Guohui","family":"Li","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Qi","family":"Chen","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8639-4570","authenticated-orcid":false,"given":"Bolong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Queensland, Brisbane, Australia"}]},{"given":"Quoc Viet Hung","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Griffith University, Gold Coast, Australia"}]},{"given":"Xiaofang","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Queensland, Brisbane, Australia"}]}],"member":"320","published-online":{"date-parts":[[2020,3,12]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. 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