Department of Computer, Control and Management Engineering

In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents a widely adopted strategy today to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classifier of Beta distributions Means (LCBM), a novel collaborative filtering algorithm for binary ratings that is (i) inherently parallelizable (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost. These characteristics allow LCBM to efficiently handle large instances of the collaborative filtering problem on a single machine in short timeframes.