{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:21:55Z","timestamp":1770780115607,"version":"3.50.0"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Program of Liaoning Provincial Department of Education","award":["LJ212510165008"],"award-info":[{"award-number":["LJ212510165008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In recommender systems research, the data sparsity problem has driven the development of hybrid recommendation algorithms integrating multimodal information and the application of graph neural networks (GNNs). However, conventional GNNs relying on homogeneous Euclidean embeddings fail to effectively model the non-Euclidean geometric manifold structures prevalent in real-world scenarios, consequently constraining the representation capacity for heterogeneous interaction patterns and compromising recommendation accuracy. As a consequence, the representation capability for heterogeneous interaction patterns is restricted, thereby affecting the overall representational power and recommendation accuracy of the models. In this paper, we propose a hyperbolic graph neural network model with contrastive learning for rating\u2013review recommendation, implementing a dual-graph construction strategy. First, it constructs a review-aware graph to integrate rich semantic information from reviews, thus enhancing the recommendation system\u2019s context awareness. Second, it builds a user\u2013item interaction graph to capture user preferences and item characteristics. The hyperbolic graph neural network architecture enables joint learning of high-order features from these two graphs, effectively avoiding the embedding distortion problem commonly associated with high-order feature learning. Furthermore, through contrastive learning in hyperbolic space, the model effectively leverages review information and user\u2013item interaction data to enhance recommendation system performance. Experimental results demonstrate that the proposed algorithm achieves excellent performance on multiple real-world datasets, significantly improving recommendation accuracy.<\/jats:p>","DOI":"10.3390\/e27080886","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T07:41:45Z","timestamp":1755848505000},"page":"886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Hyperbolic Graph Neural Network Model with Contrastive Learning for Rating\u2013Review Recommendation"],"prefix":"10.3390","volume":"27","author":[{"given":"Shuyun","family":"Fang","sequence":"first","affiliation":[{"name":"School of Software and Big Data Technology, Dalian Neusoft University of Information, Dalian 116023, China"}]},{"given":"Junling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software and Big Data Technology, Dalian Neusoft University of Information, Dalian 116023, China"}]},{"given":"Fukun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Musto, C., Rossiello, G., de Gemmis, M., Lops, P., and Semeraro, G. 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