Collaborative filtering algorithms, such as matrix factorization techniques, are recently
gaining momentum due to their promising performance on recommender systems. However …

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …

Recommender systems are promising for providing personalized favorite services.
Collaborative filtering (CF) technologies, making prediction of users' preference based on …

Collaborative filtering is a fundamental technique in recommender systems, for which
memory-based and matrix-factorization-based collaborative filtering are the two types of …

In recommendation systems, probabilistic matrix factorization (PMF) is a state-of-the-art
collaborative filtering method by determining the latent features to represent users and …

Collaborative Filtering (CF) can be achieved by Matrix Factorization (MF) with high
prediction accuracy and scalability. Most of the current MF based recommenders, however …

Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering
(CF)-based recommender systems. The performance of matrix MF methods depends on how …

Recommender systems that predict the preference of users have attracted more and more
attention in decades. One of the most popular methods in this field is collaborative filtering …

The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …

Matrix Factorization (MF) based Collaborative Filtering (CF) have proved to be a highly
accurate and scalable approach to recommender systems. In MF based CF, the learning …