Recommender systems have the effect of guiding users in a personalized way to interesting
objects in a large space of possible options. Content-based recommendation systems try to …

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 …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

News consumption has shifted over time from traditional media to online platforms, which
use recommendation algorithms to help users navigate through the large incoming streams …

User-generated content has dominated the web's recent growth and today the so-called real-
time web provides us with unprecedented access to the real-time opinions, views, and …

When recommending news items, most of the traditional algorithms are based on TF-IDF, ie,
a term-based weighting method which is mostly used in information retrieval and text mining …

Recommender systems are built to provide the most proper item or information within the
huge amount of data on the internet without the manual effort of the users. As a specific …

As users may have different needs in different situations and contexts, it is increasingly
important to consider user context data when filtering information. In the field of web …

Assigning bug reports to individual developers is typically a manual, time-consuming, and
tedious task. In this paper, we present a framework for automated assignment of bug-fixing …

Online news reading has become a popular way to read news articles from a huge
collection of news sources around the globe. News recommender systems help users …