Effective Hierarchical Vector-based News Representation for Personalized Recommendation

Mária Bieliková, Michal Kompan, Dušan Zeleník

With amount of information on the web, users often require functionality able to filter the content according to their preferences. To solve the problem of overwhelmed users we propose a content-based recommender. Our method for the personalized recommendation is dedicated to the domain of news on the Web. We propose an effective representation of news and a user model which are used to recommend dynamically changing large number of text documents. We work with the vector representation of the news and hierarchical representation of similarities among items. Our representation is designed with aim to effectively estimate user needs and generate personalized list of items in information space. This approach is unique thanks its low complexity and ability to work in real-time with no visible delay for the user. To evaluate our approach we experimented with real information space of largest Slovak newspaper and simulated recommending.