{"type":"ScholarlyArticle","title":"Modeling Contemporaneous Basket Sequences with Twin Networks for Next-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":"School of Information Systems , Singapore Management University , Singapore"}],"abstract":"Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases).  Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence.  This task is known as next-item recommendation.  We hypothesize two means for improvement.  First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them.  Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence (e.g., clicks).  We develop three twin network structures modeling the generation of both target and support basket sequences.  One based on \"Siamese networks\" facilitates full sharing of parameters between the two sequence types.  The other two based on \"fraternal networks\" facilitate partial sharing of parameters.  Experiments on real-world datasets show significant improvements upon baselines relying on one sequence type.","doi":"10.24963/ijcai.2018/474","url":"https://ijcai.org/proceedings/2018/474","pdf":"https://ijcai.org/proceedings/2018/0474.pdf","markdown":"https://ijcai.org/proceedings/2018/474.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/474","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 Applications","track":"Main track","number":474,"pages":{"first":3414,"last":3420},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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