News Recommending based on Text Similarity and user Behaviour

In this paper we describe a method for recommending news on a news portal based on our novel representation by a similarity tree. Our method for recommending articles is based on their content. The recommendation employs a hierarchical incremental clustering which is used to discover additional information for effective recommending. The important and novel part of our method is an approach to discovering the interests of individual readers using tree structure created according to similarity of articles. We concentrate on enabling the recommendations in any time, i.e. we discover user’s interests real-time. Our method discovers specific interests of the reader using information gained from monitoring his activities in the news portal. We describe the mechanisms for recommending up-to-date and relevant articles. It is based on known solutions, but incorporates unique representation of user interests by binary tree. Moreover, our aim was to provide recommendations in real-time. Recommendations are thus generated depending on the actual reader’s interest. We also present an evaluation of recommendations in the experiment where we use accounts of real readers and their history of reading.