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 useritem 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...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Robert M. Bell, Yehuda Koren, Chris Volinsky
• Keywords: Data Mining, KDD 2007, Models Involve Estimation, Netflix Cinematch Recommender, Recommender Systems