Matrix factorization on user-item rating matrices has achieved significant success in
collaborative filtering based recommendation tasks. However, it also encounters the …

Recommender systems have attracted lots of attention since they alleviate the information
overload problem for users. Matrix factorization is one of the most widely employed …

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 …

Matrix factorization (MF) technique has been widely used in collaborative filtering
recommendation systems. However, MF still suffers from data sparsity problem. Although …

Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that
has been successfully exploited in various applications, but sometimes they fail to provide …

Collaborative Filtering (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …

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

Matrix factorization (MF) is a popular collaborative filtering approach for recommender
systems due to its simplicity and effectiveness. Existing MF methods either assume that all …

Matrix Factorization (MF) based approaches have proven to be efficient for rating-based
recommendation systems. In this work, we propose several matrix factorization approaches …

Extensive work on matrix factorization (MF) techniques have been done recently as they
provide accurate rating prediction models in recommendation systems. Additional …