{"type":"ScholarlyArticle","title":"Enhancing Sequential Recommendation with Graph Contrastive Learning","authors":[{"name":"Yixin Zhang","slug":"yixin-zhang","given":"Yixin","family":"Zhang","affiliation":"School of Software , Shandong University , China"},{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"Alibaba-NTU Singapore JRI & LILY Research Centre , Nanyang Technological University , Singapore"},{"name":"Yonghui Xu","slug":"yonghui-xu","given":"Yonghui","family":"Xu","affiliation":"Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"},{"name":"Hao Xiong","slug":"hao-xiong","given":"Hao","family":"Xiong","affiliation":"Alibaba Group , China"},{"name":"Chenyi Lei","slug":"chenyi-lei","given":"Chenyi","family":"Lei","affiliation":"Alibaba Group , China"},{"name":"Wei He","slug":"wei-he","given":"Wei","family":"He","affiliation":"School of Software , Shandong University , China"},{"name":"Lizhen Cui","slug":"lizhen-cui","given":"Lizhen","family":"Cui","affiliation":"School of Software , Shandong University , China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":"Alibaba-NTU Singapore JRI & LILY Research Centre , Nanyang Technological University , Singapore; School of Computer Science and Engineering , Nanyang Technological University , Singapore"}],"abstract":"The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.","doi":"10.24963/ijcai.2022/333","url":"https://ijcai.org/proceedings/2022/333","pdf":"https://ijcai.org/proceedings/2022/0333.pdf","markdown":"https://ijcai.org/proceedings/2022/333.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/333","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":333,"pages":{"first":2398,"last":2405},"keywords":["Data Mining: Recommender Systems","Machine Learning: Recommender Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986655","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984798","length_sec":335,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. 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