The continued growth of online content makes personalized recommendation an
increasingly important tool for media consumption. While collaborative filtering techniques …

Notwithstanding the success of collaborative filtering algorithms for item recommendation
there are still situations in which there is a need for content-based recommendation …

Collaborative filtering methods have been applied to a number of domains like books,
videos, audio CDs and Usenet news. These systems require some effort on the part of users …

On-line readers are in need of tools to help them cope with the mass of content available on
the World-Wide Web. In traditional media readers are provided assistance in making …

Recommender systems have defined our choices online whether buying a book on Amazon
or choosing a movie on Netflix or reading an article on Medium. However, they are still in a …

Despite recommender systems based on collaborative filtering typically outperform content-
based systems in terms of recommendation quality, they suffer from the new item problem …

Collaborative and content-based filtering are the major methods in recommender systems
that predict new items that users would find interesting. Each method has advantages and …

Since the late 20th century, the number of Internet users has increased dramatically, as has
the number of Web searches performed on a daily basis and the amount of information …

Abstract Data Mining, or Knowledge Discovery as it is also known, is becoming increasingly
useful in a wide variety of applications. In the following paper, we look at its use in …

In this paper, we argue that the performance of content-based news recommender systems
has been hampered by using relatively old and simple matching algorithms. Using more …