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

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

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 …

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) 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 …

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 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 …

Collaborative filtering recommendation with implicit feedbacks (eg, clicks, views, and plays)
is regarded as one of the most challenging issues in both academia and industry. From …

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), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …