Matrix Factorization Techniques for Recommender Systems. Authors: Olfa Nasraoui, Explainable Matrix Factorization for Collaborative Filtering, Proceedings of the 25th International Conference Companion on World Wide Web, April 11-15, 2016, Montreal, Quebec, Canada Bo Yuan, Fast 2) Both are different ways to learn a low-rank structure in recommendation problems. 3) Once there is a fixed-rank matrix factorization, say X = UV'. Additional constraints, such as non-negativity, on U and V lead to dealing other problem scenarios like learning of low-rank images [Non-negative matrix factorization]. Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing Structured Low-Rank Matrix Factorization relaxation of matrix rank and can optimally recover low-rank matrices under certain conditions (Recht et al.,2010). ROBUST LOW-RANK MATRIX FACTORIZATION WITH MISSING DATA BY MINIMIZING L1 LOSS APPLIED TO COLLABORATIVE FILTERING Shama Mehnaz Huda, M.S. University of Pittsburgh, 2011 In this age of information overload and plethora of choices, people increasingly rely on automatic recommender systems to tell them what suits their needs. Tutorial on Collaborative Filtering and Matrix Factorization in Python April 25, 2016 This article will be of interest to you if you want to learn about recommender systems and predicting movie ratings (or book ratings, or product ratings, or any other kind of rating). Using Low Rank Matrix Factorization for Collaborative Filtering Recommender System July 2, 2016 July 4, 2016 / Sandipan Dey In this article, low rank matrix factorization will be used to predict the unrated movie ratings for the users from MovieLense (100k) dataset (given that each user has rated some of the movies). Matrix completion problem for collaborative filtering ! Over-determined -> low-rank approximation ! Rank minimization is NP-hard ! Minimize least-squares prediction for known values for given rank of matrix " Must