Click Prediction and Preference Ranking of RSS Feeds

RSS (Really Simple Syndication) is a family of data formats used to publish frequently updated works. RSS is often used for temporally relevant information updates. An RSS document consists of a series of entries, each tagged with a title, description, link, date of publication, and sometimes other metadata. However, it is often the case that a large amount of RSS feeds in aggregate will create considerably more information than the average user will be able to follow. In this case the user will read only those feeds that he or she finds interesting. However, the volume of information involved can sometimes make this search for interesting reading a time-consuming and laborious task. Several machine learning methods have been applied with some success to text categorization problems, such as Bayesian spam filters for email[1]. Here we explore text categorization algorithms for the purpose of ranking user preferences for RSS entries.