{"type":"ScholarlyArticle","title":"Basket-Sensitive Personalized Item Recommendation","authors":[{"name":"Duc-Trong Le","slug":"duc-trong-le","given":"Duc-Trong","family":"Le","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"},{"name":"Yuan Fang","slug":"yuan-fang","given":"Yuan","family":"Fang","affiliation":"‡ Institute for Infocomm Research , A*STAR , Singapore"}],"abstract":"Personalized item recommendation is useful in narrowing down the list of options provided to a user.  In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket.  Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device.  Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the basket.  Towards this goal, we propose two approaches.  First, we explore a factorization-based model called BFM that incorporates various types of associations involving the user, the target item to be recommended, and the items currently in the basket.  Second, based on our observation that various recommendations towards constructing the same basket should have similar likelihoods, we propose another model called CBFM that further incorporates basket-level constraints.  Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules.","doi":"10.24963/ijcai.2017/286","url":"https://ijcai.org/proceedings/2017/286","pdf":"https://ijcai.org/proceedings/2017/0286.pdf","markdown":"https://ijcai.org/proceedings/2017/286.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/286","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":286,"pages":{"first":2060,"last":2066},"keywords":["Machine Learning: Learning Preferences or Rankings","Multidisciplinary Topics and Applications: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 1994] Rakesh Agrawal, Ramakrishnan Srikant, et al. 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