{"type":"ScholarlyArticle","title":"ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation","authors":[{"name":"Jing Song","slug":"jing-song","given":"Jing","family":"Song","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China"},{"name":"Hong Shen","slug":"hong-shen","given":"Hong","family":"Shen","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China; School of Computer Science , The University of Adelaide , Adelaide , Australia"},{"name":"Zijing Ou","slug":"zijing-ou","given":"Zijing","family":"Ou","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China"},{"name":"Junyi Zhang","slug":"junyi-zhang","given":"Junyi","family":"Zhang","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China"},{"name":"Teng Xiao","slug":"teng-xiao","given":"Teng","family":"Xiao","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China"},{"name":"Shangsong Liang","slug":"shangsong-liang","given":"Shangsong","family":"Liang","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China"}],"abstract":"Session-based recommendation is a challenging problem due to the inherent uncertainty of user behavior and the limited historical click information. Latent factors and the complex dependencies within the user’s current session have an important impact on the user's main intention, but the existing methods do not explicitly consider this point. In this paper, we propose a novel model, Interest Shift and Latent Factors Combination Model (ISLF), which can capture the user's main intention by taking into account the user’s interest shift (i.e. long-term and short-term interest) and latent factors simultaneously. In addition, we experimentally give an explicit explanation of this combination in our ISLF. Our experimental results on three benchmark datasets show that our model achieves state-of-the-art performance on all test datasets.","doi":"10.24963/ijcai.2019/799","url":"https://ijcai.org/proceedings/2019/799","pdf":"https://ijcai.org/proceedings/2019/0799.pdf","markdown":"https://ijcai.org/proceedings/2019/799.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/799","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":"Uncertainty in AI","track":"Main track","number":799,"pages":{"first":5765,"last":5771},"keywords":["Machine Learning: Deep Learning","Machine Learning: Recommender Systems","Uncertainty in AI: Sequential Decision Making"],"videos":[],"references":[{"position":1,"raw":"[Davidson et al., 2010] James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 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