document: Modeling relationships at multiple scales to improve accuracy of large recommender systems The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past user-item relationships . In this work , we propose novel algorithms for predicting user ratings of items by integrating complementary models that focus on patterns at different scales . At a local scale , we use a neighborhood-based technique that infers ratings from observed ratings by similar users or of similar items . Unlike previous local approaches , our method is based on a formal model that accounts for interactions within the neighborhood , leading to improved estimation quality . At a higher , regional , scale , we use SVD-like matrix factorization for recovering the major structural patterns in the user-item rating matrix . Unlike previous approaches that require imputations in order to fill in the unknown matrix entries , our new iterative algorithm avoids imputation . Because the models involve estimation of millions , or even billions , of parameters , shrinkage of estimated values to account for sampling variability proves crucial to prevent overfitting . Both the local and the regional approaches , and in particular their combination through a unifying model , compare favorably with other approaches and deliver substantially better results than the commercial Netflix Cinematch recommender system on a large publicly available data set .
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