Recent Advances in Recommender Systems: Matrices, Bandits, and Blenders

Recent years have witnessed an explosion in methods applied to solve the recommendation problem. Modern recommender systems have become increasingly more complex compared to their early content-based and collaborative filtering versions. In this tutorial, we will cover recent advances in recommendation methods, focusing on matrix factorization, multi-armed bandits, and methods for blending recommendations. We will also describe evaluation techniques, and outline open issues and challenges. The ultimate goal of this tutorial is to present a toolkit of new recommendation methods in perspective to data-related problems, and highlight opportunities and new research paths for researchers and practitioners that work on problems in the intersection of recommender systems and databases.