{"type":"ScholarlyArticle","title":"Pre-training General User Representation with Multi-type APP Behaviors","authors":[{"name":"Yuren Zhang","slug":"yuren-zhang","given":"Yuren","family":"Zhang","affiliation":"State Key Laboratory of Cognitive Intelligence , University of Science and Technology of China"},{"name":"Min Hou","slug":"min-hou","given":"Min","family":"Hou","affiliation":"Hefei University of Technology"},{"name":"Kai Zhang","slug":"kai-zhang","given":"Kai","family":"Zhang","affiliation":"State Key Laboratory of Cognitive Intelligence , University of Science and Technology of China"},{"name":"Yuqing Yuan","slug":"yuqing-yuan","given":"Yuqing","family":"Yuan","affiliation":"OPPO Research Institute"},{"name":"Chao Song","slug":"chao-song","given":"Chao","family":"Song","affiliation":"OPPO Research Institute"},{"name":"Zhihao Ye","slug":"zhihao-ye","given":"Zhihao","family":"Ye","affiliation":"OPPO Research Institute"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"State Key Laboratory of Cognitive Intelligence , University of Science and Technology of China"},{"name":"Yang Yu","slug":"yang-yu","given":"Yang","family":"Yu","affiliation":"State Key Laboratory of Cognitive Intelligence , University of Science and Technology of China"}],"abstract":"In numerous user-centric services on mobile applications (apps), accurately mining user interests and generating effective user representations are paramount. Traditional approaches, which often involve training task-specific user representations, are becoming increasingly impractical due to their high computational costs and limited adaptability.\r\nThis paper introduces a novel solution to this challenge: the Multi-type App-usage Fusion Network (MAFN). MAFN innovatively pre-trains universal user representations, leveraging multi-type app behaviors to overcome key limitations in existing methods. We address two primary challenges: 1) the varying frequency of user behaviors (ranging from low-frequency actions like (un)installations to high-frequency yet insightful app launches); and 2) the integration of multi-type behaviors to form a cohesive representation. Our approach involves the creation of novel pre-training tasks that harness self-supervised signals from diverse app behaviors, capturing both long-term and short-term user interests. MAFN's unique fusion approach effectively amalgamates these interests into a unified vector space, facilitating the development of a versatile, general-purpose user representation. With a practical workflow, extensive experiments with three typical downstream tasks on real-world datasets verify the effectiveness of our approach.","doi":"10.24963/ijcai.2024/612","url":"https://ijcai.org/proceedings/2024/612","pdf":"https://ijcai.org/proceedings/2024/0612.pdf","markdown":"https://ijcai.org/proceedings/2024/612.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/612","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":"Machine Learning","track":"Main Track","number":612,"pages":{"first":5535,"last":5544},"keywords":["Data Mining: DM: Mining heterogenous data","Machine Learning: ML: Applications","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Bian et al., 2021] Shuqing Bian, Wayne Xin Zhao, Kun Zhou, Xu Chen, Jing Cai, Yancheng He, Xingji Luo, and Ji-Rong Wen. 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