Improving regularized singular value decomposition for collaborative filtering

In this paper we describe different efficient collaborative filtering techniques and a framework for combining them to obtain a good prediction. The methods described in this paper are the most im- portant parts of a solution predicting users' preferences for movies with error rate 7.04% better on the Netflix Prize dataset than the reference algorithm Netflix Cinematch.

The set of predictors used includes algorithms suggested by Netflix Prize contestants: regularized singular value de- composition of data with missing values, K-means, postpro- cessing SVD with KNN. We propose extending the set of predictors with the following methods: addition of biases to the regularized SVD, postprocessing SVD with kernel ridge regression, using a separate linear model for each movie, and using methods similar to the regularized SVD, but with fewer parameters.