Socially Driven News Recommendation
Nuno Moniz, Lu\'is Torgo, Magdalini Eirinaki

TL;DR
This paper introduces a framework for predicting the importance of news stories upon publication, focusing on recent and popular news, and enhances news recommendation quality by integrating social information.
Contribution
It presents a novel integrated framework for early importance prediction of news and combines social data to improve recommendation accuracy.
Findings
Framework effectively predicts news importance upon publication
Social information integration improves ranking quality
Enhanced recommendations outperform state-of-the-art systems
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWeb Data Mining and Analysis · Video Analysis and Summarization · Recommender Systems and Techniques
