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 …

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

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

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

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 …

The data sparsity is an acute challenge in most of the collaborative filterings (CFs) as their
performance is affected by the known ratings of target users. Recently, active learning has …

Collaborative Filtering (CF) can be achieved by Matrix Factorization (MF) with high
prediction accuracy and scalability. Most of the current MF based recommenders, however …

The traditional collaborative filtering (CF) method based on static user preference modeling
and linear matching function learning severely limits the recommendation performance. To …

The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …