{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:08:35Z","timestamp":1765544915192},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Knowledge graph (KG) is introduced as side information into recommender systems, which can alleviate the sparsity and cold start problems in collaborative filtering. Existing studies mainly focus on modeling users\u2019 historical behavior data and KG-based propagation. However, they have the limitation of ignoring noise information during recommendation. We consider that noise exists in two parts (i.e. KG and user-item interaction data). In this paper, we propose Knowledge-aware Dual-Channel Graph Neural Networks (KDGNN) to improve the recommendation performance by reducing the noise in the recommendation process. Specifically, (1) for the noise in KG, we design a personalized gating mechanism, namely dual-channel balancing mechanism, to block the propagation of redundant information in KG. (2) For the noise in user-item interaction data, we integrate personalized and knowledge-aware signals to capture user preferences fully and use personalized knowledge-aware attention to denoise user-item interaction data. Compared with existing KG-based methods, we aim to propose a knowledge-aware recommendation method from a new perspective of denoising. We perform performance analysis on three real-world datasets, and experiment results demonstrate that KDGNN achieves strongly competitive performance compared with several compelling state-of-the-art baselines.<\/jats:p>","DOI":"10.1093\/comjnl\/bxad085","type":"journal-article","created":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T08:03:21Z","timestamp":1691827401000},"page":"1607-1618","source":"Crossref","is-referenced-by-count":4,"title":["Knowledge-Aware Dual-Channel Graph Neural Networks For Denoising Recommendation"],"prefix":"10.1093","volume":"67","author":[{"given":"Hanwen","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"}]},{"given":"Li-e","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"},{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"},{"name":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"}]},{"given":"Zhigang","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"},{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"},{"name":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"}]},{"given":"Xianxian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"},{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"},{"name":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University , No. 15, Yucai Road, Guilin 541004 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"2024062312365843300_ref1","first-page":"1835","article-title":"DKN: deep knowledge-aware network for news recommendation","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Wang","year":"2018"},{"key":"2024062312365843300_ref2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1145\/2623330.2623758","article-title":"Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)","volume-title":"The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data 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