Improved neighborhood-based algorithms for large-scale recommender systems

Neighborhood-based algorithms are frequently used modules of recommender systems. Usually, the choice of the similarity measure used for evaluation of neighborhood relationships is crucial for the success of such approaches. In this article we propose a way to calculate similarities by formulating a regression problem which enables us to extract the similarities from the data in a problem-specific way. Another popular approach for recommender systems is regularized matrix factorization (RMF). We present an algorithm -- neighborhood-aware matrix factorization -- which efficiently includes neighborhood information in a RMF model. This leads to increased prediction accuracy. The proposed methods are tested on the Netflix dataset.