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However, traditional KG-aware recommendation models based on GNNs fail to utilize the dependencies of items and item attributes to model user preferences at a fine-grained level, which will result in a lack of interpretability in the model\u2019s recommendations to users. In addition, traditional KG-aware recommendation models based on GNNs fail to mine supervision signals from the perspective of user preferences and item attributes, which will result in a lack of effective supervision signals in the model. In this study, we utilize a combination of items and attributes behind the items to model user preferences at a fine-grained level, so as to achieve independence between different user preferences. Furthermore, we utilize the KG and the user\u2013item interaction graph (UIIG) to construct the user-specific preference similarity view and the item-specific attribute correlation views, respectively, and then apply the contrastive learning framework to effectively mine the association signals between users and between items. Based on this, we propose a novel model named Knowledge Graph Fine-grained Modeling Network with Contrastive Learning (KGFM-CL). Extensive experiments conducted on two real-world datasets demonstrate that KGFM-CL significantly outperforms state-of-the-art baseline models.<\/jats:p>","DOI":"10.1145\/3744926","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T06:14:09Z","timestamp":1750400049000},"page":"1-18","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge Graph Fine-grained Modeling Network with Contrastive Learning for Recommendation"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0008-8324-2422","authenticated-orcid":false,"given":"Xiya","family":"Bu","sequence":"first","affiliation":[{"name":"School of Software Technology, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-8013-4372","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Technology, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015394"},{"key":"e_1_3_1_3_2","unstructured":"Yixin Cao Lei Hou Juanzi Li and Zhiyuan Liu. 2018. 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