{"type":"ScholarlyArticle","title":"DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives","authors":[{"name":"Leilei Ding","slug":"leilei-ding","given":"Leilei","family":"Ding","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Dazhong Shen","slug":"dazhong-shen","given":"Dazhong","family":"Shen","affiliation":"Shanghai Artificial Intelligence Laboratory"},{"name":"Chao Wang","slug":"chao-wang","given":"Chao","family":"Wang","affiliation":"Guangzhou HKUST Fok Ying Tung Research Institute; School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Tianfu Wang","slug":"tianfu-wang","given":"Tianfu","family":"Wang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Le Zhang","slug":"le-zhang","given":"Le","family":"Zhang","affiliation":"Business Intelligence Lab , Baidu Research"},{"name":"Yanyong Zhang","slug":"yanyong-zhang","given":"Yanyong","family":"Zhang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"}],"abstract":"Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology. However, these models often face the famous over-smoothing issue, leading to indistinct user and item embeddings and reduced personalization. Traditional desmoothing methods in GCN-based systems are model-specific, lacking a universal solution. This paper introduces a novel, model-agnostic approach named Desmoothing Framework for GCN-based Recommendation Systems (DGR). It effectively addresses over-smoothing on general GCN-based recommendation models by considering both global and local perspectives. Specifically, \r\nwe first introduce vector perturbations during each message passing layer to penalize the tendency of node embeddings approximating overly to be similar with the guidance of the global topological structure. Meanwhile,  we further develop a tailored-design loss term for the readout embeddings to preserve the local collaborative relations between users and their neighboring items. \r\nIn particular, items that exhibit a high correlation with neighboring items are also incorporated to enhance the local topological information.\r\nTo validate our approach, we conduct extensive experiments on 5 benchmark datasets based on 5 well-known GCN-based recommendation models, demonstrating the effectiveness and generalization of our proposed framework. Our code is available at https://github.com/me-sonandme/DGR.","doi":"10.24963/ijcai.2024/224","url":"https://ijcai.org/proceedings/2024/224","pdf":"https://ijcai.org/proceedings/2024/0224.pdf","markdown":"https://ijcai.org/proceedings/2024/224.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/224","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":224,"pages":{"first":2027,"last":2035},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Collaborative filtering"],"videos":[],"references":[{"position":1,"raw":"[Abbasnejad et al., 2020] Ehsan Abbasnejad, Damien Teney, Amin Parvaneh, Javen Shi, and Anton van den Hengel. 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