Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

Nowadays, more and more news readers read news online where they have access to
millions of news articles from multiple sources. In order to help users find the right and …

More and more people read the news online, eg, by visiting the websites of their favorite
newspapers or by navigating the sites of news aggregators. However, the abundance of …

Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …

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 …

Personalized news recommendation methods are widely used in online news services.
These methods usually recommend news based on the matching between news content …

Recommender systems help users deal with information overload by providing tailored item
suggestions to them. The recommendation of news is often considered to be challenging …

News recommender systems are aimed to personalize users experiences and help them to
discover relevant articles from a large and dynamic search space. Therefore, news domain …

Although the matrix completion paradigm provides an appealing solution to the collaborative
filtering problem in recommendation systems, some major issues, such as data sparsity and …

A major challenge in collaborative filtering based recommender systems is how to provide
recommendations when rating data is sparse or entirely missing for a subset of users or …