Matrix and Tensor Factorization Techniques for Recommender Systems

Abstract

Representing data in lower dimensional spaces has been used extensively in many disciplines such as natural language and image processing, datamining, and information retrieval. Recommender systems deal with challenging issues such as scalability, noise, and sparsity and thus, matrix and tensor factorization techniques appear as an interesting tool to be exploited. That is, we can deal with all afore- mentioned challenges by applying matrix and tensor decomposition methods (also known as factorization methods). In this chapter, we provide some basic defini- tions and preliminary concepts on dimensionality reductionmethods of matrices and tensors. Gradient descent and alternating least squares methods are also discussed. Finally, we present the book outline and the goals of each chapter.