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 …

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 …

Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF)
algorithm, is one of the most widely accepted approaches to provide service …

The item-based collaborative filtering technique recommends an item to the user from the
rating of k-nearest items. Generally, a random value of k is considered to find nearest …

Memory-based collaborative filtering is one of the recommendation system methods used to
predict a user's rating or preference by exploring historic ratings, but without incorporating …

The collaborative filtering (CF) based models are capable of grasping the interaction or
correlation of users and items under consideration. However, existing CF-based methods …

Memory-based approaches for collaborative filtering identify the similarity between two
users by comparing their ratings on a set of items. In the past, the memory-based approach …

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

Abstract One-Class Collaborative Filtering (OCCF) has recently received much attention in
recommendation communities due to their close relationship with real industry problem …