Towards a journalist-based news recommendation system: The Wesomender approach

The present paper introduces a context-aware recommendation system for journalists to enable the identification of similar topics across different sources. More specifically a journalist-based recommendation system that can be automatically configured is presented to exploit news according to expert preferences. News contextual features are also taken into account due to the their special nature: time, current user interests, location or existing trends are combined with traditional recommendation techniques to provide an adaptive framework that deals with heterogeneous data providing an enhanced collaborative filtering system. Since the Wesomender approach is able to generate context-aware recommendations in the journalism field, a quantitative evaluation with the aim of comparing Wesomender results with the expectations of a team of experts is also performed to show that a context-aware adaptive recommendation engine can fulfil the needs of journalists daily work when retrieving timely and primary information is required.