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

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) techniques have already shown its strong foundation in
collaborative filtering (CF), particularly for rating prediction problem. In the basic MF model …

It is today accepted that matrix factorization models allow a high quality of rating prediction in
recommender systems. However, a major drawback of matrix factorization is its static nature …

One of the most efficient methods in collaborative filtering is matrix factorization, which finds
the latent vector representations of users and items based on the ratings of users to items …

Matrix factorization is one of the most powerful techniques in collaborative filtering, which
models the (user, item) interactions behind historical explicit or implicit feedbacks. However …

One of the typical goals of collaborative filtering algorithms is to produce rating predictions
with values very close to what real users would give to an item. Afterward, the items having …

Collaborative filtering was initially proposed as a framework for filtering information based
on the preferences of users, and has since been refined in many different ways. This thesis …

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

Matrix approximation is one of the most effective methods for collaborative filtering-based
recommender systems. However, the high computation complexity of matrix factorization on …