{"type":"ScholarlyArticle","title":"A Bayesian Latent Variable Model of User Preferences with Item Context","authors":[{"name":"Aghiles Salah","slug":"aghiles-salah","given":"Aghiles","family":"Salah","affiliation":"School of Information Systems , Singapore Management University , Singapore"},{"name":"Hady W. Lauw","slug":"hady-w-lauw","given":"Hady W.","family":"Lauw","affiliation":"School of Information Systems , Singapore Management University , Singapore"}],"abstract":"Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse.  We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users' choices.  Intuitively, items that occur within the same \"context\" (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect.  Therefore, accounting for the item's context would complement the sparse user-item interactions by extending a user's preference to other items of similar aspects.  To realize this intuition, we develop Collaborative Context Poisson Factorization (C2PF), a new Bayesian latent variable model that seamlessly integrates contextual relationships among items into a personalized recommendation approach. We further derive a scalable variational inference algorithm to fit C2PF to preference data.  Empirical results on real-world datasets show evident performance improvements over strong factorization models.","doi":"10.24963/ijcai.2018/370","url":"https://ijcai.org/proceedings/2018/370","pdf":"https://ijcai.org/proceedings/2018/0370.pdf","markdown":"https://ijcai.org/proceedings/2018/370.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/370","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":370,"pages":{"first":2667,"last":2674},"keywords":["Machine Learning: Learning Generative Models","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Bishop, 2006] Christopher M Bishop. 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