{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:02:22Z","timestamp":1777705342098,"version":"3.51.4"},"reference-count":2,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user\u2019s motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users\u2019 check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments.<\/jats:p>","DOI":"10.3233\/jifs-232491","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T11:13:30Z","timestamp":1702638810000},"page":"3307-3321","source":"Crossref","is-referenced-by-count":3,"title":["DLAN:Modeling user long- and short-term preferences based on double-layer attention network for next point-of-interest recommendation"],"prefix":"10.1177","volume":"46","author":[{"given":"Yuhang","family":"Wu","sequence":"first","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of General Education, Tianjin Foreign Studies University, Tianjin, China"},{"name":"Department of Computer Science, Norwegian University of Science and Technology, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingbo","family":"Hao","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingyuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenguang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-232491_ref19","first-page":"1944","article-title":"Personalized long-and short-term preference learning for next POI Recommendation,Trans. Knowl. Data Eng.","volume":"34","author":"Yuxia Wu","year":"2022","journal-title":"IEEE"},{"key":"10.3233\/JIFS-232491_ref30","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TSMC.2014.2327053","article-title":"Modeling user activity preference by leveraging user spatialtemporal characteristics in lbsns","volume":"45","author":"Dingqi Yang","year":"2015","journal-title":"IEEE Transactions onSystems, Man, and Cybernetics: Systems"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232491","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:34Z","timestamp":1777455814000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232491"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,14]]},"references-count":2,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232491","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,14]]}}}