{"type":"ScholarlyArticle","title":"Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network","authors":[{"name":"Zheng Liu","slug":"zheng-liu","given":"Zheng","family":"Liu","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Yu Xing","slug":"yu-xing","given":"Yu","family":"Xing","affiliation":"University of Science and Technology China , Hefei , China"},{"name":"Fangzhao Wu","slug":"fangzhao-wu","given":"Fangzhao","family":"Wu","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Mingxiao An","slug":"mingxiao-an","given":"Mingxiao","family":"An","affiliation":"University of Science and Technology China , Hefei , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research Asia , Beijing , China"}],"abstract":"Deep learning techniques have been widely applied to modern recommendation systems, bringing in flexible and effective ways of user representation. Conventionally, user representations are generated purely in the offline stage. Without referencing to the specific candidate item for recommendation, it is difficult to fully capture user preference from the perspective of interest. More recent algorithms tend to generate user representation at runtime, where user's historical behaviors are attentively summarized w.r.t. the presented candidate item. In spite of the improved efficacy, it is too expensive for many real-world scenarios because of the repetitive access to user's entire history. In this work, a novel user representation framework, Hi-Fi Ark, is proposed. With Hi-Fi Ark, user history is summarized into highly compact and complementary vectors in the offline stage, known as archives. Meanwhile, user preference towards a specific candidate item can be precisely captured via the attentive aggregation of such archives. As a result, both deployment feasibility and superior recommendation efficacy are achieved by Hi-Fi Ark. The effectiveness of Hi-Fi Ark is empirically validated on three real-world datasets, where remarkable and consistent improvements are made over a variety of well-recognized baseline methods.","doi":"10.24963/ijcai.2019/424","url":"https://ijcai.org/proceedings/2019/424","pdf":"https://ijcai.org/proceedings/2019/0424.pdf","markdown":"https://ijcai.org/proceedings/2019/424.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/424","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":424,"pages":{"first":3059,"last":3065},"keywords":["Humans and AI: Personalization and User Modeling","Machine Learning: Data Mining","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Bansal et al., 2016] Trapit Bansal, David Belanger, and An- drew McCallum. 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