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Inf. Syst."],"published-print":{"date-parts":[[2022,1,31]]},"abstract":"<jats:p>\n            Next\n            <jats:bold>Point-of-interest (POI)<\/jats:bold>\n            recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world\n            <jats:bold>location-based social networks (LBSNs)<\/jats:bold>\n            datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3464300","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T15:31:23Z","timestamp":1631115083000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["CHA: Categorical Hierarchy-based Attention for Next POI Recommendation"],"prefix":"10.1145","volume":"40","author":[{"given":"Hongyu","family":"Zang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Bejing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongcheng","family":"Han","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Bejing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4257-4347","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Bejing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Wan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Bejing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhong","family":"Wang","sequence":"additional","affiliation":[{"name":"University of the Sunshine Coast, Queensland, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2016.7849919"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3231933"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.06.065"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304226"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence(January 25\u201330","author":"Chen Xuefeng","year":"2015","unstructured":"Xuefeng Chen , Yifeng Zeng , Gao Cong , Shengchao Qin , Yanping Xiang , and Yuanshun Dai . 2015 . 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POI2Vec: Geographical latent representation for predicting future visitors. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (February 4\u20139, 2017, San Francisco, CA), Satinder P. Singh and Shaul Markovitch (Eds.). AAAI Press, 102\u2013108. http:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14902"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219902"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence (February 12\u201317","author":"He Jing","year":"2016","unstructured":"Jing He , Xin Li , Lejian Liao , Dandan Song , and William K. Cheung . 2016. Inferring a personalized next point-of-interest recommendation model with latent behavior patterns . In Proceedings of the 30th AAAI Conference on Artificial Intelligence (February 12\u201317 , 2016 , Phoenix, AZ). 137\u2013143. Jing He, Xin Li, Lejian Liao, Dandan Song, and William K. Cheung. 2016. 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