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Dec 2, 2012

How Netflix Makes Recommendation for "Movies you will love"

In late October, Xavier Amatriain from Netflix gave a talk at the ACM bay area chapter data mining SIG monthly meeting. He touched on interested algorithms used at Netflix for recommending movies to the users.

The two core algorithms used are restricted Boltzmann Machine (RBM) and Singular Value Decomposition (SVD). Restricted Boltzmann Machine is a type of neural network with 1-hidden layer (See an illustration of below). In Netflix case, one RBM is built for every user. The input node for each RBM is a movie that user has rated. 

Singular Value Decomposition is a special method of decomposing a matrix into 2 or 3 matrices (depending on whether there is a diagonal matrix). In Netflix case, it decomposes the original user-movie rating matrix (where row is users, and columns correspond to movies) into 2 sub-matrices, one is a user factor matrix and one contains movie factors. This is useful when there are many missing values in the original matrix, which is the case when a user only rate a small amount of movies. The decomposed new matrices are smaller and have all cells filled with non-zero values.

Both methods with their special application for Netflix data were originally proposed in the paper titled "Restricted Boltzmann Machines for Collaborative Filtering" by Ruslan Salakhutdinov and his colleagues, presented in the International Conference of Machine Learning (ICML) in 2007. This papers presented some scalable computing methods, and thus was adopted by Netflix.

The full slides of Xavier Amatriain's talk is available here.

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