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

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 an accurate and scalable approach for collaborative filtering
(CF)-based recommender systems. The performance of matrix MF methods depends on how …

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 …

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) is one of the most popular techniques used in Collaborative
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …

Matrix factorization (MF) methods have shown great promise in collaborative filtering (CF).
Conventional MF methods usually assume that the correlated data is distributed on a linear …

We propose a probabilistic matrix factorization model for collaborative filtering that learns
from data that is missing not at random (MNAR). Matrix factorization models exhibit state-of …

Collaborative filtering algorithms, such as matrix factorization techniques, are recently
gaining momentum due to their promising performance on recommender systems. However …