A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model

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摘要

In this paper we present a novel technique for predicting the tastes of users in recommender systems based on collaborative filtering. Our technique is based on factorizing the rating matrix into two non negative matrices whose components lie within the range [0, 1] with an understandable probabilistic meaning. Thanks to this decomposition we can accurately predict the ratings of users, find out some groups of users with the same tastes, as well as justify and understand the recommendations our technique provides.

论文关键词:Recommender systems,Collaborative filtering,Matrix factorization,Graphical probabilistic models

论文评审过程:Received 12 April 2015, Revised 16 November 2015, Accepted 25 December 2015, Available online 6 January 2016, Version of Record 20 February 2016.

论文官网地址:https://doi.org/10.1016/j.knosys.2015.12.018