High Performance Offline and Online Distributed Collaborative Filtering

Big data analytics is a hot research area both in academia and industry. It envisages processing massive amounts of data at high rates to generate new insights leading to positive impact (for both users and providers) of industries such as E-commerce, Telecom, Finance, Life Sciences and so forth. We consider collaborative filtering (CF) and Clustering algorithms that are key fundamental analytics kernels that help in achieving these aims. High throughput CF and co-clustering on highly sparse and massive datasets, along with a high prediction accuracy, is a computationally challenging problem. In this paper, we present a novel hierarchical design for soft real-time (less than 1-minute.) distributed co-clustering based collaborative filtering algorithm. We study both the online and offline variants of this algorithm. Theoretical analysis of the time complexity of our algorithm proves the efficacy of our approach. Further, we present the impact of load balancing based optimizations on multi-core cluster architectures. Using the Netflix dataset(900M training ratings with replication) as well as the Yahoo KDD Cup(2.3B training ratings with replication) datasets, we demonstrate the performance and scalability of our algorithm on a large multi-core cluster architecture. In offline mode, our distributed algorithm demonstrates around 4x better performance (on Blue Gene/P) as compared to the best prior work, along with high accuracy. In online mode, we demonstrated around 3x better performance compared to baseline MPI implementation. To the best of our knowledge, our algorithm provides the best known online and offline performance and scalability results with high accuracy on multi-core cluster architectures.