User ratings on items are noisy in real-world recommender systems, which raises
challenges to matrix approximation (MA)-based collaborative filtering (CF) algorithms—the …

Gradient-based learning methods such as stochastic gradient descent are widely used in
matrix approximation-based collaborative filtering algorithms to train recommendation …

Recommender systems have achieved great success in recent years, and matrix
approximation (MA) is one of the most popular techniques for collaborative filtering (CF) …

Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among
today's collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item …

Matrix approximation (MA) is one of the most popular techniques for collaborative filtering
(CF). Most existing MA methods train user/item latent factors based on a user-item rating …

Collaborative filtering (CF) methods for recommendation systems have been extensively
researched, ranging from matrix factorization and autoencoder-based to graph filtering …

Matrix approximation (MA) methods are integral parts of today's recommender systems. In
standard MA methods, only one feature vector is learned for each user/item, which may not …

Collaborative filtering (CF) has been widely employed within recommender systems to solve
many real-world problems. Learning effective latent factors plays the most important role in …

Matrix factorization as a popular technique for collaborative filtering in recommendation
systems computes the latent factors for users and items by decomposing a user-item rating …

In this paper, we propose a Bayesian Deep Collaborative Matrix Factorization (BDCMF)
algorithm for collaborative filtering (CF). BDCMF is a novel Bayesian deep generative model …