{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:09:30Z","timestamp":1779286170071,"version":"3.51.4"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62276193 and 62576254"],"award-info":[{"award-number":["62276193 and 62576254"]}],"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. Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    The next\n                    <jats:bold>Point-of-Interest (POI)<\/jats:bold>\n                    recommendation is a hotspot for both industry and academia, which helps users better experience the physical world. However, existing methods suffer from a severe bias towards recommending repeat POIs that have been visited by the target user before, and perform inefficiently when recommending new POIs that have not been visited by the target user yet. To overcome this issue, we delve into the next new POI recommendation and uncover the coexistence of local and global exploration patterns in users\u2019 visits to new POIs, showing their willingness to explore not only nearby new POIs but also those distant ones. Subsequently, we develop a novel\n                    <jats:bold>\n                      Local and Global Exploration (\n                      <jats:sc>LGE<\/jats:sc>\n                      )\n                    <\/jats:bold>\n                    framework for the next new POI recommendation. In particular,\n                    <jats:sc>LGE<\/jats:sc>\n                    involves three key modules: (1) a\n                    <jats:bold>Zone-Aware Local Exploration (ZLE)<\/jats:bold>\n                    module, which encourages users to explore POIs in the local area by learning zone-aware POI representations and regularizing POI prediction with zone information; (2) an\n                    <jats:bold>Intention-Aware Global Exploration (IGE)<\/jats:bold>\n                    module, which recommends POIs that meet user intentions without distance constraints by extracting static and dynamic intentions from category information; (3) a fusion module, which contains a\n                    <jats:bold>Mean Pooling (MP)<\/jats:bold>\n                    strategy and a\n                    <jats:bold>Weighted Pooling (WP)<\/jats:bold>\n                    strategy to aggregate the outputs of local and global exploration modules for the final recommendation. Experiments carried out on real-world datasets have shown the effectiveness of\n                    <jats:sc>LGE<\/jats:sc>\n                    in recommending new POIs.\n                  <\/jats:p>","DOI":"10.1145\/3807950","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:16:59Z","timestamp":1776079019000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Local and Global Exploration for Next New POI Recommendation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2051-6296","authenticated-orcid":false,"given":"Ke","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3635-3077","authenticated-orcid":false,"given":"Liyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Naval University of Engineering, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1877-224X","authenticated-orcid":false,"given":"Mayi","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"3570","volume-title":"IEEE Transactions on Knowledge and Data Engineering","volume":"37","author":"Chen Wei","year":"2025","unstructured":"Wei Chen, Haoyu Huang, Zhiyu Zhang, Tianyi Wang, Youfang Lin, Liang Chang, and Huaiyu Wan. 2025. Next-POI recommendation via spatial-temporal knowledge graph contrastive learning and trajectory prompt. IEEE Transactions on Knowledge and Data Engineering 37 (2025), 3570\u20133582."},{"key":"e_1_3_2_3_2","first-page":"262","volume-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","volume":"2","author":"Chen Yile","year":"2025","unstructured":"Yile Chen, Yicheng Tao, Yue Jiang, Shuai Liu, Han Yu, and Gao Cong. 2025. Enhancing large language models for mobility analytics with semantic location tokenization. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Vol. 2, 262\u2013273."},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the 23rd 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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