{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:38:40Z","timestamp":1776184720098,"version":"3.50.1"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62276193"],"award-info":[{"award-number":["62276193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            Existing sequential\n            <jats:bold>Point of Interest (POI)<\/jats:bold>\n            recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully exploring city signature can highlight the characteristics of each city and facilitate cross-city complementary learning. To this end, we consider the two-city scenario and propose a\n            <jats:bold>Dual-Target Cross-City Sequential POI Recommendation<\/jats:bold>\n            model\n            <jats:bold>(DCSPR)<\/jats:bold>\n            to achieve the purpose of complementary learning across cities. On one hand,\n            <jats:sc>DCSPR<\/jats:sc>\n            respectively captures\n            <jats:italic>geographical and cultural characteristics<\/jats:italic>\n            for each city by mining intra-city regions and intra-city functions of POIs. On the other hand,\n            <jats:sc>DCSPR<\/jats:sc>\n            builds\n            <jats:italic>a transfer channel<\/jats:italic>\n            between cities based on intra-city functions, and adopts a novel transfer strategy to transfer useful cultural characteristics across cities by mining inter-city functions of POIs. Moreover, to utilize these captured characteristics for sequential POI recommendation,\n            <jats:sc>DCSPR<\/jats:sc>\n            involves a new\n            <jats:italic>region- and function-aware network<\/jats:italic>\n            for each city to learn transition patterns from multiple views. Extensive experiments conducted on two real-world datasets with four cities demonstrate the effectiveness of\n            <jats:sc>DCSPR<\/jats:sc>\n            .\n          <\/jats:p>","DOI":"10.1145\/3664284","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T15:31:46Z","timestamp":1715355106000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["City Matters! A Dual-Target Cross-City Sequential POI Recommendation Model"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2051-6296","authenticated-orcid":false,"given":"KE","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-6374","authenticated-orcid":false,"given":"Chenliang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4667-5794","authenticated-orcid":false,"given":"Tieyun","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557262"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467132"},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the 23th International Joint Conference on Artificial Intelligence","author":"Cheng Chen","year":"2013","unstructured":"Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. 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