Semantic coherence-based user profile modeling in the recommender systems context

Recommender systems usually produce their results to the users based on the interpretation of the whole historic interactions of these. This canonical approach sometimes could lead to wrong results due to several factors, such as a changes in user taste over time or the use of her/his account by third parties. This work proposes a novel dynamic coherence-based approach that analyzes the information stored in the user profiles based on their coherence. The main aim is to identify and remove from the previously evaluated items those not adherent to the average preferences, in order to make a user profile as close as possible to the user's real tastes. The conducted experiments show the effectiveness of our approach to remove the incoherent items from a user profile, increasing the recommendation accuracy.