{"type":"ScholarlyArticle","title":"Self-supervised Graph Disentangled Networks for Review-based Recommendation","authors":[{"name":"Yuyang Ren","slug":"yuyang-ren","given":"Yuyang","family":"Ren","affiliation":"Shanghai Jiao Tong University"},{"name":"Haonan Zhang","slug":"haonan-zhang","given":"Haonan","family":"Zhang","affiliation":"Shanghai Jiao Tong University"},{"name":"Qi Li","slug":"qi-li","given":"Qi","family":"Li","affiliation":"Shanghai Jiao Tong University"},{"name":"Luoyi Fu","slug":"luoyi-fu","given":"Luoyi","family":"Fu","affiliation":"Shanghai Jiao Tong University"},{"name":"Xinbing Wang","slug":"xinbing-wang","given":"Xinbing","family":"Wang","affiliation":"Shanghai Jiao Tong University"},{"name":"Chenghu Zhou","slug":"chenghu-zhou","given":"Chenghu","family":"Zhou","affiliation":"Institute of Geographical Sciences and Natural Resources Research , Chinese Academy of Sciences {renyuyang , zhanghaonan ,"}],"abstract":"User review data is considered as auxiliary information to alleviate the data sparsity problem and improve the quality of learned user/item or interaction representations in review-based recommender systems. However, existing methods usually model user-item interactions in a holistic manner and neglect the entanglement of the latent intents behind them, e.g., price, quality, or appearance, resulting in suboptimal representations and reducing interpretability. In this paper, we propose a Self-supervised Graph Disentangled Networks for review-based recommendation (SGDN), to separately model the user-item interactions based on the latent factors through the textual review data. To this end, we first model the distributions of interactions over latent factors from both semantic information in review data and structural information in user-item graph data, forming several factor graphs. Then a factorized message passing mechanism is designed to learn disentangled user/item and interaction representations on the factor graphs. Finally, we set an intent-aware contrastive learning task to alleviate the sparsity issue and encourage disentanglement through dynamically identifying positive and negative samples based on the learned intent distributions. Empirical results over five benchmark datasets validate the superiority of  SGDN over the state-of-the-art methods and the  interpretability of learned intent factors.","doi":"10.24963/ijcai.2023/254","url":"https://ijcai.org/proceedings/2023/254","pdf":"https://ijcai.org/proceedings/2023/0254.pdf","markdown":"https://ijcai.org/proceedings/2023/254.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/254","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":254,"pages":{"first":2288,"last":2295},"keywords":["Data Mining: DM: Collaborative filtering","Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Berg et al., 2017] Rianne van den Berg, Thomas N Kipf, and Max Welling. 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