Recently, Deep Neural Networks (DNNs) have been largely utilized in Collaborative
Filtering (CF) to produce more accurate recommendation results due to their ability of …

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 …

As the deep learning techniques have expanded to real-world recommendation tasks, many
deep neural network based Collaborative Filtering (CF) models have been developed to …

Recommender systems based on Collaborative Filtering (CF) techniques have achieved
great success in e-commerce, social networks and various other applications on the Web …

Collaborative filtering (CF) is one of the most effective and popular recommendation
methods. Deep learning has achieved great success in various tasks and extensive work …

Recently, neighborhood-based collaborative filtering has been increasingly used in
personalized recommender systems. However, inevitably, the neighborhood selection is …

Numerous state-of-the-art recommendation frameworks employ deep neural networks in
Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network …

Collaborative Filtering (CF), a well-known approach in producing recommender systems,
has achieved wide use and excellent performance not only in research but also in industry …

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 …

Among various recommender techniques, collaborative filtering (CF) is the most successful
one. And a key problem in CF is how to represent users and items. Previous works usually …