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 …

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 …

Matrix factorization (MF) technique has been widely used in collaborative filtering
recommendation systems. However, MF still suffers from data sparsity problem. Although …

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

Matrix factorization (MF) is a popular collaborative filtering approach for recommender
systems due to its simplicity and effectiveness. Existing MF methods either assume that all …

Recommendation techniques play a vital role in recommending an actual product to an
intended user. The recommendation also supports the user in the decision-making process …

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 …

Matrix factorization on user-item rating matrices has achieved significant success in
collaborative filtering based recommendation tasks. However, it also encounters the …

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

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