Introduction to Matrix Factorization for Recommender Systems

Recommender systems aim to personalize the experience of user by suggesting items to the user based on

the preferences of a user. The preferences are learned from the user’s interaction history or through explicit

ratings that the user has given to the items. The system could be part of a retail website, an online bookstore,

a movie rental service or an online education portal and so on. In this paper, I will focus on matrix

factorization algorithms as applied to recommender systems and discuss the singular value decomposition,

gradient descent-based matrix factorization and parallelizing matrix factorization for large scale

applications.