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

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 …

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

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 …

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 …

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 …

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

Collaborative filtering (CF) has been extensively studied in recommendation, spawning
various solutions. While graph convolution networks (GCNs) are effective at representation …

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

One of the most popular approaches to Collaborative Filtering is based on Matrix
Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy …