文章基本信息

标题：Binary Matrix Factorization applied to Netflix dataset analysis
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作者：Mamadou Diop ; Sebastian Miron ; Anthony Larue 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：24
页码：13-17
DOI：10.1016/j.ifacol.2019.12.368
语种：English
出版社：Elsevier
摘要：In this paper we aim at assessing the potential of Binary Matrix Factorization (BMF) in the implementation of recommendation systems, by analyzing a Netflix dataset. In particular, we study the explanatory power and the prediction capability of a particular BMF algorithm based on a post non-linear mixture model, namely the Post NonLinear Penalty Function (PNL-PF) algorithm. Unlike the majority of BMF methods, PNL-PF is capable of efficiently handling the difficult case of correlated rank-1 binary terms. We show that BMF represents an interesting alternative to classical matrix factorization methods in terms of explanatory power and prediction capability.
关键词：KeywordsBinary Matrix FactorizationRecommendation system