Matrix factorization Perhaps the most common type of recommender system algorithm is matrix factorization. The idea behind matrix factorization is to break a user-item feature matrix into a ...

The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their ...

Matrix factorization in numerical linear algebra (NLA) typically serves the purpose of restating some given problem in such a way that it can be solved more readily; for example, one major application ...