{"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-aware-dual-side-attribute-enhanced","title":"Knowledge-aware Dual-side Attribute-enhanced Recommendation","arxiv_id":"2403.16037","date":"2024-03-24","proceeding":null,"authors":["Taotian Pang","Xingyu Lou","Fei Zhao","Zhen Wu","Kuiyao Dong","Qiuying Peng","Yue Qi","Xinyu Dai"],"abstract":"\\textit{Knowledge-aware} recommendation methods (KGR) based on \\textit{graph neural networks} (GNNs) and \\textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-grained user preferences and further fail to leverage the \\textit{preference-attribute connection} to make predictions, leading to sub-optimal performance. To address the issue, we propose a method named \\textit{\\textbf{K}nowledge-aware \\textbf{D}ual-side \\textbf{A}ttribute-enhanced \\textbf{R}ecommendation} (KDAR). Specifically, we build \\textit{user preference representations} and \\textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \\textit{collaborative filtering} (CF) based user and item representations, respectively. To discriminate the contribution of each attribute in these two types of attribute-based representations, a \\textit{multi-level collaborative alignment contrasting} mechanism is proposed to align the importance of attributes with CF signals. Experimental results on four benchmark datasets demonstrate the superiority of KDAR over several state-of-the-art baselines. Further analyses verify the effectiveness of our method. The code of KDAR is released at: \\href{https://github.com/TJTP/KDAR}{https://github.com/TJTP/KDAR}.","url_abs":"https://arxiv.org/abs/2403.16037v1","url_pdf":"https://arxiv.org/pdf/2403.16037v1.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-aware-dual-side-attribute-enhanced","repo_url":"https://github.com/tjtp/kdar","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"knowledge-aware-recommendation","task_name":"Knowledge-Aware Recommendation"}],"methods":[{"method_slug":"align","method_name":"ALIGN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}