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 …

Recommender systems are promising for providing personalized favorite services.
Collaborative filtering (CF) technologies, making prediction of users' preference based on …

Collaborative Filtering (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …

ABSTRACT Recommendation Systems apply Information Retrieval techniques to select the
online information relevant to a given user. Collaborative Filtering (CF) is currently most …

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 …

Recommender systems, which can significantly help users find their interested items from
the information era, has attracted an increasing attention from both the scientific and …

Recommender Systems are useful information filtering tools that have reduced information
overload over the web. Collaborative filtering (CF) is one of the extensively used …

Collaborative Filtering (CF) is the most widely used prediction technique in recommender
systems. It makes recommendations based on ratings that users have assigned to items …

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

Factorization-and neighborhood-based methods have been recognized as state-of-the-art
approaches for collaborative recommendation tasks. In this article, the authors take user …