I am very glad to present a tutorial about “Matrix and Tensor Decomposition Techniques in Recommender Systems” on September 19th 2016 in RecSys conference.
The tutorial will offer a rich blend of theory and practice regarding dimensionality reduction methods, to address the information overload problem in recommender systems. This problem affects our everyday experience while searching for knowledge on a topic. Naive Collaborative Filtering cannot deal with challenging issues such as scalability, noise, and sparsity. We can deal with all the aforementioned challenges by applying matrix and tensor decomposition methods (also known as factorization methods). These methods have been proven to be the most accurate (i.e., Netflix prize) and efficient for handling big data. For each method (SVD, SVD++, timeSVD++, HOSVD, CUR, etc.) we will provide a detailed theoretical mathematical background and a step-by-step analysis, by using an integrated toy example, which runs throughout all parts of the tutorial, and helps the audience to understand clearly the differences among different factorization methods.
Each part of this tutorial provides the audience with an introduction to the most important aspects of Matrix and Tensor Factorization techniques in Recommender Systems and also contains many valuable references to relevant research papers. It also provides researchers and developers a comprehensive overview of the general concepts and techniques (e.g., models and algorithms) related with Matrix and Tensor Factorization recommendation and present them all new methods through real-life application scenarios and toy examples.