Bayesian deep collaborative matrix factorization
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 …
algorithm for collaborative filtering (CF). BDCMF is a novel Bayesian deep generative model …
Deep collaborative filtering via marginalized denoising auto-encoder
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 …
many real-world problems. Learning effective latent factors plays the most important role in …
Neural variational matrix factorization with side information for collaborative filtering
Abstract Probabilistic Matrix Factorization (PMF) is a popular technique for collaborative
filtering (CF) in recommendation systems. The purpose of PMF is to find the latent factors for …
filtering (CF) in recommendation systems. The purpose of PMF is to find the latent factors for …
A hybrid collaborative filtering model with deep structure for recommender systems
Collaborative filtering (CF) is a widely used approach in recommender systems to solve
many real-world problems. Traditional CF-based methods employ the user-item matrix …
many real-world problems. Traditional CF-based methods employ the user-item matrix …
Bayesian dual neural networks for recommendation
Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to
make recommendations, which usually suffer from cold-start and sparsity problems. To …
make recommendations, which usually suffer from cold-start and sparsity problems. To …
Kernelized probabilistic matrix factorization for collaborative filtering: exploiting projected user and item graph
B Pal, M Jenamani - Proceedings of the 12th ACM conference on …, 2018 - dl.acm.org
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 for rating prediction problem. In the basic MF model …
Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering
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 …
intended user. The recommendation also supports the user in the decision-making process …
Decomposed collaborative filtering: Modeling explicit and implicit factors for recommender systems
Representation learning is the keystone for collaborative filtering. The learned
representations should reflect both explicit factors that are revealed by extrinsic attributes …
representations should reflect both explicit factors that are revealed by extrinsic attributes …
[PDF][PDF] Sparse probabilistic matrix factorization by laplace distribution for collaborative filtering
L Jing, P Wang, L Yang - Twenty-Fourth International Joint Conference on …, 2015 - ijcai.org
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 method by determining the latent features to represent users and …
GLOMA: Embedding global information in local matrix approximation models for collaborative filtering
Recommender systems have achieved great success in recent years, and matrix
approximation (MA) is one of the most popular techniques for collaborative filtering (CF) …
approximation (MA) is one of the most popular techniques for collaborative filtering (CF) …