{"type":"ScholarlyArticle","title":"Self-supervised Graph Neural Networks for Multi-behavior Recommendation","authors":[{"name":"Shuyun Gu","slug":"shuyun-gu","given":"Shuyun","family":"Gu","affiliation":"Beijing University of Posts and Telecommunications"},{"name":"Xiao Wang","slug":"xiao-wang","given":"Xiao","family":"Wang","affiliation":"Beijing University of Posts and Telecommunications"},{"name":"Chuan Shi","slug":"chuan-shi","given":"Chuan","family":"Shi","affiliation":"Beijing University of Posts and Telecommunications"},{"name":"Ding Xiao","slug":"ding-xiao","given":"Ding","family":"Xiao","affiliation":"Beijing University of Posts and Telecommunications"}],"abstract":"Traditional recommendation usually focuses on utilizing only one target user behavior (e.g., purchase) but ignoring other auxiliary behaviors (e.g., click, add to cart). Early efforts of multi-behavior recommendation often emphasize the differences between multiple behaviors, i.e., they aim to extract useful information by distinguishing different behaviors. However, the commonality between them, which reflects user's common preference for items associated with different behaviors, is largely ignored. Meanwhile, the multi-behavior recommendation still severely suffers from limited supervision signal issue. In this paper, we propose a novel self-supervised graph collaborative filtering model for multi-behavior recommendation named S-MBRec. Specifically, for each behavior, we execute the GCNs to learn the user and item embeddings. Then we design a supervised task, distinguishing the importance of different behaviors, to capture the differences between embeddings. Meanwhile, we propose a star-style contrastive learning task to capture the embedding commonality between target and auxiliary behaviors, so as to alleviate the sparsity of supervision signal, reduce the redundancy among auxiliary behavior, and extract the most critical information. Finally, we jointly optimize the above two tasks. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of S-MBRec, where the maximum improvement can reach to 20%.","doi":"10.24963/ijcai.2022/285","url":"https://ijcai.org/proceedings/2022/285","pdf":"https://ijcai.org/proceedings/2022/0285.pdf","markdown":"https://ijcai.org/proceedings/2022/285.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/285","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":285,"pages":{"first":2052,"last":2058},"keywords":["Machine Learning: Self-supervised Learning","Data Mining: Mining Graphs","Machine Learning: Recommender Systems","Data Mining: Recommender Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986896","length_sec":70,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985039","length_sec":317,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen et al., 2020a] Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 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