{"type":"ScholarlyArticle","title":"Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation","authors":[{"name":"Lianyong Qi","slug":"lianyong-qi","given":"Lianyong","family":"Qi","affiliation":"College of Computer Science and Technology , China University of Petroleum (East China); Yunnan Key Laboratory of Service Computing; School of Computer Science , Qufu Normal University"},{"name":"Yuwen Liu","slug":"yuwen-liu","given":"Yuwen","family":"Liu","affiliation":"College of Computer Science and Technology , China University of Petroleum (East China)"},{"name":"Weiming Liu","slug":"weiming-liu","given":"Weiming","family":"Liu","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Shichao Pei","slug":"shichao-pei","given":"Shichao","family":"Pei","affiliation":"University of Massachusetts Boston"},{"name":"Xiaolong Xu","slug":"xiaolong-xu","given":"Xiaolong","family":"Xu","affiliation":"School of Computer and Software , Nanjing University of Information Science and Technology"},{"name":"Xuyun Zhang","slug":"xuyun-zhang","given":"Xuyun","family":"Zhang","affiliation":"Department of Computing , Macquarie University"},{"name":"Yingjie Wang","slug":"yingjie-wang","given":"Yingjie","family":"Wang","affiliation":"School of Computer and Control Engineering , Yantai University"},{"name":"Wanchun Dou","slug":"wanchun-dou","given":"Wanchun","family":"Dou","affiliation":"Department of Computer Science and Technology , Nanjing University"}],"abstract":"With the proliferation of Location-based Social Networks (LBSNs), user check-in data at Points-of-Interest (POIs) has surged, offering rich insights into user preferences. However, sequential POI recommendation systems always face two pivotal challenges. A challenge lies in the difficulty of modeling time in a discrete space, which fails to accurately capture the dynamic nature of user preferences. Another challenge is the inherent sparsity and noise in continuous POI recommendation, which hinder the recommendation process. To address these challenges, we propose counterfactual user sequence synthesis with continuous time dynamic preference modeling (CussCtpm). CussCtpm innovatively combines Gated Recurrent Unit (GRU) with neural Ordinary Differential Equations (ODEs) to model user preferences in a continuous time framework. CussCtpm captures user preferences at both the POI-level and interest-level, identifying deterministic and non-deterministic preference concepts. Particularly at the interest-level, we employ GRU and neural ODEs to model users' dynamic preferences in continuous space, aiming to capture finer-grained shifts in user preferences over time. Furthermore, CussCtpm utilizes counterfactual data augmentation to generate counterfactual positive and negative user sequences. Our extensive experiments on two widely-used public datasets demonstrate that CussCtpm outperforms several advanced baseline models.","doi":"10.24963/ijcai.2024/255","url":"https://ijcai.org/proceedings/2024/255","pdf":"https://ijcai.org/proceedings/2024/0255.pdf","markdown":"https://ijcai.org/proceedings/2024/255.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/255","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":"Data Mining","track":"Main Track","number":255,"pages":{"first":2306,"last":2314},"keywords":["Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Ahnert and Mulansky, 2011] Karsten Ahnert and Mario Mulansky. 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