In the last 16 years, more than 200 research articles were published about research-paper
recommender systems. We reviewed these articles and present some descriptive statistics in …

Online vendors employ collaborative filtering algorithms to provide recommendations to their
customers so that they can increase their sales and profits. Although recommendation …

Recently, recommender systems are widely used on various platforms in real world to
provide personalized recommendations. However, sparsity is a tough problem in a …

Recommender systems are an important part of the information and e-commerce ecosystem.
They represent a powerful method for enabling users to filter through large information and …

In this age of information overload, people use a variety of strategies to make choices about
what to buy, how to spend their leisure time, and even whom to date. Recommender …

The importance of contextual information has been recognized by researchers and
practitioners in many disciplines, including e-commerce personalization, information …

As one of the most successful approaches to building recommender systems, collaborative
filtering (CF) uses the known preferences of a group of users to make recommendations or …

This paper presents an overview of the field of recommender systems and describes the
current generation of recommendation methods that are usually classified into the following …

Recommender systems have been evaluated in many, often incomparable, ways. In this
article, we review the key decisions in evaluating collaborative filtering recommender …

Recommender systems are becoming increasingly important to individual users and
businesses for providing personalized recommendations. However, while the majority of …