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 …

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) is a simple collaborative filtering technique that achieves superior
recommendation accuracy by decomposing the user–item interaction matrix into user and …

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

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

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

The advantage of Factorization Machines over other factorization models is their ability to
easily integrate and efficiently exploit auxiliary information to improve Collaborative Filtering …

Matrix factorization (MF) methods have proven as efficient and scalable approaches for
collaborative filtering problems. Numerous existing MF methods rely heavily on explicit …

Matrix approximation is a common model-based approach to collaborative filtering in
recommender systems. Many relevant algorithms that fuse social contextual information …

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 …