{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:43:52Z","timestamp":1760060632083,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Science Fund of Ministry of Education","award":["24YJCZH416","62403076"],"award-info":[{"award-number":["24YJCZH416","62403076"]}]},{"name":"National Natural Science Foundation of China","award":["24YJCZH416","62403076"],"award-info":[{"award-number":["24YJCZH416","62403076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Next-point-of-interest (POI) recommendation aims to model user preferences based on historical information to predict future mobility behavior, which has significant application value in fields such as urban planning, traffic management, and optimizing business decisions. However, existing methods often overlook the differences in location, time, and category information features, fail to fully utilize information from various modalities, and lack effective solutions for addressing users\u2019 incidental behavior. Additionally, existing methods are somewhat lacking in capturing users\u2019 personalized preferences. To address these issues, we propose a new method called Multi-Branch Spectral Network with Contrastive Learning (MBSCL-Net) for next-POI recommendation. We use a multihead attention mechanism to separately capture the distinct features of location, time, and category information, and then fuse the captured features to effectively integrate cross-modal features, avoid feature confusion, and achieve effective modeling of multi-modal information. We propose converting the time-domain information of user check-ins into frequency-domain information through Fourier transformation, directly enhancing the low-frequency signals of users\u2019 periodic behavior and suppressing occasional high-frequency noise, thereby greatly alleviating noise interference caused by the introduction of too much information. Additionally, we introduced contrastive learning loss to distinguish user behavior patterns and better model personalized preferences. Extensive experiments on two real-world datasets demonstrate that MBSCL-Net outperforms state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3390\/s25185613","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T19:16:17Z","timestamp":1757445377000},"page":"5613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MBSCL-Net: Multi-Branch Spectral Network and Contrastive Learning for Next-Point-of-Interest Recommendation"],"prefix":"10.3390","volume":"25","author":[{"given":"Sucheng","family":"Wang","sequence":"first","affiliation":[{"name":"Elite Engineering School, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3457-1982","authenticated-orcid":false,"given":"Jinlai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zeng","sequence":"additional","affiliation":[{"name":"Elite Engineering School, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s11704-018-8011-2","article-title":"Next: A neural network framework for next poi recommendation","volume":"14","author":"Zhang","year":"2020","journal-title":"Front. 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