In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

Collaborative filtering is a popular strategy in recommender systems area. This approach
gathers users' ratings and then predicts what users will rate based on their similarity to other …

This paper proposes a matrix-based collaborative filtering (CF) employing personal values
(MCFPV). Introduction of various factors such as diversity and long-tailedness in addition to …

Many of the recent algorithms have been developed to improve the various aspects of
collaborative filtering recommender systems, however, most of them do not take the …

Matrix Factorization (MF) is one of the most popular techniques used in Collaborative
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …

Item-based collaborative filtering has been widely used in practice and is becoming the most
promising approach in recommender systems. It predicts a user's interest for a target item …

Matrix factorization (MF) is one of the well-known methods in collaborative filtering to build
accurate and efficient recommender systems. While in all the previous studies about MF …

Recommender systems are essential tools for many e-commerce services, such as Amazon,
Netflix, etc. to recommend new items to users. Among various recommendation techniques …

Memory-based methods for collaborative filtering predict new ratings by averaging
(weighted) ratings between, respectively, pairs of similar users or items. In practice, a large …

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting
recommendation services. The conventional CF approach has limitations, namely data …