Abstract: A rich set of data can be acquired by suitable technology, integrated with RSS reader implementations, to better understand the web feed consuming audience. In turn, that data can be applied to help publishers better understand their readership. Embodiments of the present invention are directed to capturing user data, generate predictions on how an article would be received by the readership (preferably before it is published), and automatically create recommendations for use by an author or web publisher on how the article might be edited or “fine tuned” to achieve greater impact. This conserves precious time for publishers by giving predictions and thus helps generate more relevant content for the readership. In one embodiment, we build a profile for each author/publisher over her entire set of published articles. So when a new article is written, it is matched against this author profile and recommendations are made. The rollups are done on all articles published by a single author.

Abstract: An RSS reader ranks articles and RSS feeds based on monitoring user interactions with each article. In an enterprise version, ranking can reflect the interactions of multiple users with RSS feeds and articles. Monitored user interactions can include reading an article, tagging, forwarding, emailing and the like.