Proximal maximum margin matrix factorization for collaborative filtering

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• 作者：Vikas Kumar^a ; ^{vikas@uohyd.ac.in} ; Arun K. Pujari^a ; ^b ; ^{akpcs@uohyd.ernet.in} ; Sandeep Kumar Sahu^a ; ^{uusandeepsahu@uohyd.ac.in} ; Venkateswara Rao Kagita^a ; ^{venkateswar.rao.kagita@gmail.com} ; Vineet Padmanabhan ; ^a ; ^{vineetcs@uohyd.ernet.in}
• 关键词：Collaborative filtering ; Matrix completion ; Matrix factorization
• 刊名：Pattern Recognition Letters
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：86
• 期：Complete
• 页码：62-67
• 全文大小：1003 K
• 卷排序：86

文摘

We propose an alternative and new MMMF scheme for discrete-valued rating matrix. Our work draws motivation of recent advent of proximal support vector machines. The propose method overcomes the problem of overtting. We validate our hypothesis by conducting experiments on real and synthetic datasets.