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Learning to Predict Movie Ratings from the Netflix Dataset

In this paper, we describe a hybrid recommendation system combining the two main approaches to recommendation collaborative filtering and content-based classification. We build a collaborative filtering framework to construct a useritem matrix of ratings and produce recommendations based on user-user similarity computed using Pearson correlation. We tackle the sparsity of the user-item matrix by incorporating a NaiveBayes classifier for each user and using it to predict the unknown ratings in the user-item matrix. We presents results from applying our approach to a movie recommendation database provided by the Netflix movie rental service.