Fabio Petroni, Leonardo Querzoni, Roberto Beraldi and Mario Paolucci: "LCBM: Statistics-based Parallel Collaborative Filtering." In: Proceedings of the 17th International Conference on Business Information Systems (BIS), 2014.

Abstract: In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents today a widely adopted strategy 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 and (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost.

Fabio Petroni and Leonardo Querzoni: "GASGD: Stochastic Gradient Descent for Distributed Asynchronous Matrix Completion via Graph Partitioning." In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys), 2014. (to appear)

Abstract: In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents today's a widely adopted strategy 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 and (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost.