{"type":"ScholarlyArticle","title":"Sequential Recommender Systems: Challenges, Progress and Prospects","authors":[{"name":"Shoujin Wang","slug":"shoujin-wang","given":"Shoujin","family":"Wang","affiliation":"Department of Computing , Macquarie University"},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"Advanced Analytics Institute , University of Technology Sydney; University of Shanghai for Science and Technology"},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":"Department of Computing , Macquarie University"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"Advanced Analytics Institute , University of Technology Sydney"},{"name":"Quan Z. Sheng","slug":"quan-z-sheng","given":"Quan Z.","family":"Sheng","affiliation":"Department of Computing , Macquarie University"},{"name":"Mehmet Orgun","slug":"mehmet-orgun","given":"Mehmet","family":"Orgun","affiliation":"Department of Computing , Macquarie University"}],"abstract":"The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.","doi":"10.24963/ijcai.2019/883","url":"https://ijcai.org/proceedings/2019/883","pdf":"https://ijcai.org/proceedings/2019/0883.pdf","markdown":"https://ijcai.org/proceedings/2019/883.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/883","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":"Survey track","track":"Survey track","number":883,"pages":{"first":6332,"last":6338},"keywords":["Humans and AI: Personalization and User Modeling","Machine Learning: Recommender Systems","Uncertainty in AI: Sequential Decision Making","Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2018] Xu Chen, Hongteng Xu, Yongfeng Zhang, and et al. 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