Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …

On the Internet, where the number of choices is overwhelming, there is need to filter,
prioritize and efficiently deliver relevant information in order to alleviate the problem of …

The MovieLens datasets are widely used in education, research, and industry. They are
downloaded hundreds of thousands of times each year, reflecting their use in popular press …

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 …

A recommender system (RS) aims to provide personalized recommendations to users for
specific items (eg, music, books). Popular techniques involve content-based (CB) models …

In collaborative filtering recommender systems user's preferences are expressed as ratings
for items, and each additional rating extends the knowledge of the system and affects the …

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 …

Since their introduction in the early 1990's, automated recommender systems have
revolutionized the marketing and delivery of commerce and content by providing …

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 …

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 …