  The participatory Web has enabled the ubiquitous and pervasive access of
information, accompanied by an increase of speed and reach in information
sharing. Data dissemination services such as news aggregators are expected to
provide up-to-date, real-time information to the end users. News aggregators
are in essence recommendation systems that filter and rank news stories in
order to select the few that will appear on the users front screen at any time.
One of the main challenges in such systems is to address the recency and
latency problems, that is, to identify as soon as possible how important a news
story is. In this work we propose an integrated framework that aims at
predicting the importance of news items upon their publication with a focus on
recent and highly popular news, employing resampling strategies, and at
translating the result into concrete news rankings. We perform an extensive
experimental evaluation using real-life datasets of the proposed framework as
both a stand-alone system and when applied to news recommendations from Google
News. Additionally, we propose and evaluate a combinatorial solution to the
augmentation of official media recommendations with social information. Results
show that the proposed approach complements and enhances the news rankings
generated by state-of-the-art systems.
