{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:09:39Z","timestamp":1779174579429,"version":"3.51.4"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Grab-NUS AI Lab, a joint collaboration between GrabTaxi Holdings Pte. Ltd. and National University of Singapore, and the Industrial Postgraduate Program","award":["S18-1198-IPP-II"],"award-info":[{"award-number":["S18-1198-IPP-II"]}]},{"DOI":"10.13039\/501100001446","name":"Economic Development Board of Singapore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001446","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting relations between users and POIs, can help to alleviate this sparsity problem. In this article, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we propose a selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Further, we propose a novel Joint Triplet Loss Learning (JTLL) module to learn visited and unvisited relations between users and POIs for the recommendation task. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our proposed approach significantly outperforms baseline and the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3610584","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T15:41:14Z","timestamp":1690472474000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning Hierarchical Spatial Tasks with Visiting Relations for Next POI Recommendation"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9323-1091","authenticated-orcid":false,"given":"Nicholas","family":"Lim","sequence":"first","affiliation":[{"name":"GrabTaxi Holdings, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5645-1754","authenticated-orcid":false,"given":"Bryan","family":"Hooi","sequence":"additional","affiliation":[{"name":"Institute of Data Science and School of Computing, National University of Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6565-7511","authenticated-orcid":false,"given":"See-Kiong","family":"Ng","sequence":"additional","affiliation":[{"name":"Institute of Data Science and School of Computing, National University of Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6640-8744","authenticated-orcid":false,"given":"Yong Liang","family":"Goh","sequence":"additional","affiliation":[{"name":"GrabTaxi Holdings, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4812-829X","authenticated-orcid":false,"given":"Renrong","family":"Weng","sequence":"additional","affiliation":[{"name":"GrabTaxi Holdings, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5793-0307","authenticated-orcid":false,"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"GrabTaxi Holdings, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"3301","volume-title":"IJCAI","author":"Chang Buru","year":"2018","unstructured":"Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, and Jaewoo Kang. 2018. 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