{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:59:14Z","timestamp":1764053954696,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Industrial Support Project of Gansu Colleges","award":["2022CYZC-11","61762078"],"award-info":[{"award-number":["2022CYZC-11","61762078"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022CYZC-11","61762078"],"award-info":[{"award-number":["2022CYZC-11","61762078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Session-based recommendation (SBR) aims to predict a user\u2019s next item of interest by analyzing their anonymous browsing patterns. While previous studies have demonstrated considerable efficacy, they may fall short when confronted with exceedingly sparse interaction data. This paper presents a novel approach, cross-session graph and hypergraph co-guided session-based recommendation (CGH-SBR), which adeptly forecasts subsequent items while upholding efficiency and precision. First, we construct a directed graph that captures sequential dependencies by modeling cross-session item transitions, alongside building a hypergraph that encapsulates higher-order relationships between items within sessions. Subsequently, we employ two distinct graph neural networks (GNNs) to learn item representations on these two graphs separately. Further, we innovate by integrating a symmetry-aware co-guided learning framework. This framework promotes the integration of diverse perspectives and facilitates mutual learning, leveraging the data\u2019s symmetric properties to enhance the model\u2019s pattern recognition capabilities. Comprehensive experimentation conducted on two public datasets showcases the outstanding performance and potential of the recommendation system presented by CGH-SBR.<\/jats:p>","DOI":"10.3390\/sym17030389","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T10:43:31Z","timestamp":1741085011000},"page":"389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-Session Graph and Hypergraph Co-Guided Session-Based Recommendation"],"prefix":"10.3390","volume":"17","author":[{"given":"Pingrong","family":"Li","sequence":"first","affiliation":[{"name":"School of E-Commerce, Longnan Normal University, Longnan 742500, China"}]},{"given":"Huifang","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xin, X., Yang, L., Zhao, Z.Q., Ren, P.J., Chen, Z.M., Ma, J., and Ren, Z.C. 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