Introduction to Matrix Factorization for Recommender Systems

Abstract

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.

Shalin Shah

Introduction to Matrix Factorization for Recommender Systems (PDF)
(This tutorial was part of my course notes for a matrix analysis course at JHU)

References

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