Matrix factorization (MF) is one of the well-known methods in collaborative filtering to build
accurate and efficient recommender systems. While in all the previous studies about MF …

Matrix factorization (MF) has been demonstrated to be one of the most competitive
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …

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

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 designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that
the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF) …

ABSTRACT Recommendation Systems apply Information Retrieval techniques to select the
online information relevant to a given user. Collaborative Filtering (CF) is currently most …

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 …

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF)
techniques used in recommender systems due to its effectiveness and ability to deal with …

Matrix factorization is a popular collaborative filtering method for recommendation
techniques with predictive accuracy and good scalability. In this paper, we propose two …