Buzzer - Online Real-Time Topical News Article and Source Recommender

The significant growth of media and user-generated content online has allowed for the widespread adoption of recommender systems due to their proven ability to reduce the workload of a user and personalise content. In this paper, we describe our prototype system called Buzzer, which harnesses real-time micro-blogging activity, such as Twitter, as the basis for promoting personalised content, such as news articles, from RSS feeds. We also introduce several new features, that include a technique for recommending community articles from the pooled resources of all system users and also a mechanism for recommending source RSS feeds to which the user does not subscribe.