{"type":"ScholarlyArticle","title":"NPE: Neural Personalized Embedding for Collaborative Filtering","authors":[{"name":"ThaiBinh Nguyen","slug":"thaibinh-nguyen","given":"ThaiBinh","family":"Nguyen","affiliation":"SOKENDAI ( The Graduate University for Advanced Studies) , Japan"},{"name":"Atsuhiro Takasu","slug":"atsuhiro-takasu","given":"Atsuhiro","family":"Takasu","affiliation":"SOKENDAI ( The Graduate University for Advanced Studies) , Japan; National Institute of Informatics , Japan"}],"abstract":"Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorly for \"cold-users\" (users with little history of such interactions) and at capturing the relationships between closely related items. To address these problems, we propose a neural personalized embedding (NPE) model, which improves the recommendation performance for cold-users and can learn effective representations of items. It models a user's click to an item in two terms: the personal preference of the user for the item, and the relationships between this item and other items clicked by the user. We show that NPE outperforms competing methods for top-N recommendations, specially for cold-user recommendations. We also performed a qualitative analysis that shows the effectiveness of the representations learned by the model.","doi":"10.24963/ijcai.2018/219","url":"https://ijcai.org/proceedings/2018/219","pdf":"https://ijcai.org/proceedings/2018/0219.pdf","markdown":"https://ijcai.org/proceedings/2018/219.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/219","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":"Humans and AI","track":"Main track","number":219,"pages":{"first":1583,"last":1589},"keywords":["Machine Learning: Neural Networks","Machine Learning: Recommender Systems","Machine Learning: Data Mining","Humans and AI: Personalization and User Modeling","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Barkan and Koenigstein, 2016] Oren Barkan and Noam Koenigstein. 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