Matrix Factorization algorithms for Recommender Systems I need to learn about Matrix Factorization for recommender systems, so I downloaded this paper https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf but I found it too shallow. It didn't explain the concepts in depth for me. So can you please recommend some good papers/resources to learn about the topic?
I need to learn about them so I can implement a matrix factorization model.
 A: Matrix factorisation is part of Numerical Linear Algebra (NLA).  The following are some useful books in NLA and Data Mining / Statistical Learning.  


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*The classic in NLA is Golub & Van Loan's Matrix Computations.  Van Loan's webpage lists his books in and links to others.

*A modern approach that's great for self-study, is Numerical Linear Algebra by Trefethen & Bau, partially available online on Trefethen's  website.  Bau was working at Google last I checked.

*For a data mining focus, Numerical Linear Algebra and Applications in Data Mining by Lars Elden is available online.

*A classic on the statistical side is Elements of Statistical Learning by Hastie, Tibshirani, and Friedman.  The authors have graciously made available their entire book online.  This requires a fair bit of mathematical background, but the introductions to each topic will be accessible more generally.

*A lighter version of the above is Introduction to Statistical Learning with Applications in R, by the same authors plus Daniella Witten.  This is also available online by the authors and provides useful R code.
A: You can have a look to http://dl.acm.org/citation.cfm?id=2043956. For more details you can look into Recommender system handbook.
I am working on similar problems. For more details or discussion mail me @ pranav.waila[at]gmail[dot]com.  
