Abstract
Context-aware recommender systems aim at outperforming traditional context-free recommenders by exploiting information about the context under which the users' ratings are acquired. In this paper we present a novel contextual pre-filtering approach that takes advantage of the semantic similarities between contextual situations. For assessing context similarity we rely only on the available users' ratings and we deem as similar two contextual situations that are influencing in a similar way the user's rating behavior. We present an extensive comparative evaluation of the proposed approach using several contextually-tagged ratings data sets. We show that it outperforms state-of-the-art context-aware recommendation techniques.