{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T06:12:07Z","timestamp":1765260727302,"version":"3.46.0"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T00:00:00Z","timestamp":1765065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Chongqing Natural Science Foundation","doi-asserted-by":"crossref","award":["2024NSCQ-MSX0321"],"award-info":[{"award-number":["2024NSCQ-MSX0321"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJZD-K202501106"],"award-info":[{"award-number":["KJZD-K202501106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs) have achieved significant success, they often overlook the diversity and dynamics of user preferences. To address these issues, researchers have begun to employ Hypergraph Convolutional Networks (HGCNs) for disentangled representation learning. However, two critical problems have received less attention: (1) the limited expressive capacity of conventional hypergraph convolution layers, which restricts the modeling of complex nonlinear user\u2013POI preference interactions and consequently weakens generalization performance, and (2) the inadequate utilization of contrastive learning mechanisms, which prevents fully capturing cross-view collaborative signals and limits the exploitation of complementary multi-view information. To tackle these challenges, we propose a Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning (NE-DCHL) for next POI recommendation. The proposed model enhances nonlinear modeling capability and generalization by integrating ReLU activation, residual connections, and dropout regularization within the hypergraph convolution layer. A K-Nearest Neighbor (KNN)-based weighted adjacency matrix is employed to construct the geographical-view hypergraph, reducing computational complexity while maintaining essential spatial correlations. Moreover, a mini-batch InfoNCE loss and the GRACE (deep GRAph Contrastive rEpresentation learning) framework are utilized to improve efficiency and cross-view collaboration. Extensive experiments on two real-world datasets demonstrate that NE-DCHL consistently outperforms the original DCHL and other state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/info16121086","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T08:21:43Z","timestamp":1765182103000},"page":"1086","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NE-DCHL: Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning for Next Point-of-Interest Recommendation"],"prefix":"10.3390","volume":"16","author":[{"given":"Hongwei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guolong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Basic Sciences, Chongqing Medical and Pharmaceutical College, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cho, E., Myers, S.A., and Leskovec, J. 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