Improving regularized singular value decomposition for collaborative filtering

A key part of a recommender system is a collaborative filter- ing algorithm predicting users' preferences for items. 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. All predictors and selected 2-way interactions between them are combined using linear regression on a holdout set.