{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T03:46:39Z","timestamp":1772595999559,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:00:00Z","timestamp":1757116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:00:00Z","timestamp":1757116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Korea Advanced Institute of Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Point-of-interest\u00a0(POI) recommendation systems play an important role in various location-based services by improving the user experience. Previous research has leveraged large-scale visit records to predict a user\u2019s next visit POI based on the behavior of similar users. However, with the increasing emphasis on privacy preservation, there is a shift towards zero-shot recommendation that does not require training and only uses individual visit history data. As a better alternative to traditional zero-shot recommender systems, this paper proposes a novel zero-shot recommender system leveraging the ability of pre-trained large language models\u00a0(LLMs) to understand human behavior called\n                    <jats:italic>ZeroPOIRec<\/jats:italic>\n                    .\n                    <jats:italic>ZeroPOIRec<\/jats:italic>\n                    involves a\n                    <jats:italic>profiler module<\/jats:italic>\n                    that enables LLMs to extract individual user preferences from multiple aspects, including spatio-temporal patterns and individual characteristics, and a\n                    <jats:italic>recommender module<\/jats:italic>\n                    that enhances the zero-shot POI recommendation performance via candidate refinement and prioritization. Through experiments using a benchmark dataset and a newly introduced real-world dataset with semantic variables, we demonstrate that, despite\n                    <jats:italic>ZeroPOIRec<\/jats:italic>\n                    being a zero-shot approach, it outperforms state-of-the-art methods in terms of recommendation performance.\n                  <\/jats:p>","DOI":"10.1007\/s10618-025-01148-w","type":"journal-article","created":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T10:53:37Z","timestamp":1757156017000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large language models are zero-shot point-of-interest recommenders"],"prefix":"10.1007","volume":"39","author":[{"given":"Joeun","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngjin","family":"Seo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeonsoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhyeok","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeeho","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patara","family":"Trirat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae-Gil","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,6]]},"reference":[{"key":"1148_CR1","doi-asserted-by":"publisher","first-page":"110659","DOI":"10.1016\/j.knosys.2023.110659","volume":"275","author":"G Albora","year":"2023","unstructured":"Albora G, Mori LR, Zaccaria A (2023) Sapling similarity: a performing and interpretable memory-based tool for recommendation. 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