{"type":"ScholarlyArticle","title":"Deep Matrix Factorization Models for Recommender Systems","authors":[{"name":"Hong-Jian Xue","slug":"hong-jian-xue","given":"Hong-Jian","family":"Xue","affiliation":"National Key Laboratory for Novel Software Technology ; Nanjing University , Nanjing 210023 , China"},{"name":"Xinyu Dai","slug":"xinyu-dai","given":"Xinyu","family":"Dai","affiliation":"National Key Laboratory for Novel Software Technology ; Nanjing University , Nanjing 210023 , China"},{"name":"Jianbing Zhang","slug":"jianbing-zhang","given":"Jianbing","family":"Zhang","affiliation":"National Key Laboratory for Novel Software Technology ; Nanjing University , Nanjing 210023 , China; Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing 210023 , China"},{"name":"Shujian Huang","slug":"shujian-huang","given":"Shujian","family":"Huang","affiliation":"National Key Laboratory for Novel Software Technology ; Nanjing University , Nanjing 210023 , China"},{"name":"Jiajun Chen","slug":"jiajun-chen","given":"Jiajun","family":"Chen","affiliation":"National Key Laboratory for Novel Software Technology ; Nanjing University , Nanjing 210023 , China"}],"abstract":"Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information.  Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization model with neural network architecture. Firstly, we construct a user-item matrix with explicit ratings and non-preference implicit feedback. With this matrix as the input, we present a deep structure learning architecture to learn a common low dimensional space for the representations of users and items.  Secondly, we design a new loss function based on binary cross entropy, in which we consider both explicit ratings and implicit feedback for a better optimization.  The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other state-of-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings.","doi":"10.24963/ijcai.2017/447","url":"https://ijcai.org/proceedings/2017/447","pdf":"https://ijcai.org/proceedings/2017/0447.pdf","markdown":"https://ijcai.org/proceedings/2017/447.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/447","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 S-Z","track":"Main track","number":447,"pages":{"first":3203,"last":3209},"keywords":["Machine Learning: Data Mining","Machine Learning: Neural Networks","Natural Language Processing: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2014] Yang Bao, Hui Fang, and Jie Zhang. 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