{"type":"ScholarlyArticle","title":"A Survey on User Behavior Modeling in Recommender Systems","authors":[{"name":"Zhicheng He","slug":"zhicheng-he","given":"Zhicheng","family":"He","affiliation":"Huawei Noah's Ark Lab , Shenzhen , China"},{"name":"Weiwen Liu","slug":"weiwen-liu","given":"Weiwen","family":"Liu","affiliation":"Huawei Noah's Ark Lab , Shenzhen , China"},{"name":"Wei Guo","slug":"wei-guo","given":"Wei","family":"Guo","affiliation":"Huawei Noah's Ark Lab , Singapore"},{"name":"Jiarui Qin","slug":"jiarui-qin","given":"Jiarui","family":"Qin","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Yingxue Zhang","slug":"yingxue-zhang","given":"Yingxue","family":"Zhang","affiliation":"Huawei Noah's Ark Lab , Montreal , Canada"},{"name":"Yaochen Hu","slug":"yaochen-hu","given":"Yaochen","family":"Hu","affiliation":"Huawei Noah's Ark Lab , Montreal , Canada"},{"name":"Ruiming Tang","slug":"ruiming-tang","given":"Ruiming","family":"TANG","affiliation":"Huawei Noah's Ark Lab , Shenzhen , China"}],"abstract":"User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.","doi":"10.24963/ijcai.2023/746","url":"https://ijcai.org/proceedings/2023/746","pdf":"https://ijcai.org/proceedings/2023/0746.pdf","markdown":"https://ijcai.org/proceedings/2023/746.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/746","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":"Survey Track","track":"Survey Track","number":746,"pages":{"first":6656,"last":6664},"keywords":["Survey: Data Mining","Survey: Search"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2022] Yue Cao, Xiaojiang Zhou, and Jiaqi Feng et al. 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