Item-to-item collaborative filtering (short for ICF) has been widely used in ecommerce
websites due to his interpretability and simplicity in real-time personalized recommendation …

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems
in industry, owing to its strength in user interest modeling and ease in online …

Neighborhood-based collaborative filtering is a method of high significance among
recommender systems, with advantages of simplicity and justifiability. However, recently it is …

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation
accuracy and ease in online penalization and thus is favored by the industrial recommender …

Over the last few years, the deep neural network is utilized to solve the collaborative filtering
problem, a method of which has achieved immense success on computer vision, speech …

In this paper, we propose a deep collaborative filtering recommendation model, which
consists of an attention-based representation learning component and a multi-input …

Collaborative filtering techniques remain a staple in recommender systems research and
applications. With the plethora of research done in recommender systems, some more …

Similarity is a vital component for neighborhood-based collaborative filtering (CF). To
improve the quality of recommendation, many similarity methods have been proposed and …

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building
recommender systems in industrial settings, owing to its interpretability and efficiency in real …

The user-based collaborative filtering (UCF) model has been widely used in industry for
recommender systems. UCF predicts a user's interest in an item based on rating information …