01535nas a2200133   4500008004100000245004900041210004900090260001900139300001300158520108400171100002101255700002201276856010301298       2012                        eng d00aClustering to Deal with the New User Problem0 aClustering to Deal with the New User Problem  c5 - 7 December  a 58 - 653 aCollaborative filtering (CF) techniques attempt to alleviate information overload by identifying which items a user will find interesting to browse. It focuses on identification of other users with similar tastes and usage of their opinions in order to recommend items. Commonly, however, CF suffers from the so-called new user problem which occurs when a new user is added to the system and there is not enough information to make a good suggestion. The system has to acquire some data about the new user in order to start making personalized recommendations. In this paper, we present a novel algorithm that combines previously acquired knowledge from article and user clustering in order to quickly determine the new user's interests. We attempt to address the new user problem by providing a personalized strategy for prompting the user with articles to rate. Our approach makes use of hypernyms extracted from the WordNet database and proves to be converging fast to the actual user interests based on minimal user ratings which are provided during the registration process.1 aBouras, Christos1 aTsogkas, Vassilis  uhttps://telematics.upatras.gr/telematics/publications/clustering-deal-new-user-problem?language=el