Context-aware recommender systems aim at outperforming tradition-al context-free recommenders by exploiting information about the context under which the users' ratings are acquired. In this paper we present a novel contextu-al pre-filtering approach that takes advantage of the semantic similarities be-tween 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 ex-tensive comparative evaluation of the proposed approach using several contex-tually-tagged ratings data sets. We show that it outperforms state-of-the-art con-text-aware recommendation techniques.