{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/knowledge-refined-denoising-network-for","title":"Knowledge-refined Denoising Network for Robust Recommendation","arxiv_id":"2304.14987","date":"2023-04-28","proceeding":null,"authors":["Xinjun Zhu","Yuntao Du","YUREN MAO","Lu Chen","Yujia Hu","Yunjun Gao"],"abstract":"Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \\textit{task-irrelevant knowledge propagation} and \\textit{vulnerability to interaction noise}, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called \\textit{Knowledge-refined Denoising Network} (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL.","url_abs":"https://arxiv.org/abs/2304.14987v1","url_pdf":"https://arxiv.org/pdf/2304.14987v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"knowledge-refined-denoising-network-for","repo_url":"https://github.com/xj-zhu98/krdn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"knowledge-aware-recommendation","task_name":"Knowledge-Aware Recommendation"},{"task_slug":"model-optimization","task_name":"Model Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}