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 …

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) …

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 …

Many existing recommendation methods such as matrix factorization (MF) mainly rely on
user–item rating matrix, which sometimes is not informative enough, often suffering from the …

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

Matrix factorization is widely used in Recommender Systems. Although existing popular
incremental matrix factorization methods are effectively in reducing time complexity, they …

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

With the advent of social influence, social recommender systems have become an active
research topic for making recommendations based on the ratings of the users that have …

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