Improving novelty in streaming recommendation using a context model

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) In recent years there has been an increasing research interest in novelty/diversity detection in Information Retrieval and in Recommendation Systems. We propose a model that increases the novelty of recommendations using a context user profile that was created automatically using self-organizing maps. Our system was evaluated on the Reuters Corpus Volume 1 and our experiments show that filtering the recommended items using a novelty score derived from the contextbased user profile provides better search results in terms of novel information that is presented to the user.This work was supported by the Ministerio de Educaci on y Ciencia under the grant N. TIN2011-28538-C02, Novelty, di- versity, context and time: newdimensions in next-generation information retrieval and recommender systems