Abstract: Context-aware recommender systems aim at outperforming traditional 2-dimensional recommenders by exploiting information about the context under which the users’ ratings are acquired. In this talk I will present a novel contextual pre-filtering approach that takes advantage of the semantic similarities among contexts. For assessing context similarity we have proposed a method, based only on the available users’ ratings, which estimates as similar two contextual conditions that are influencing in a similar way the user’s rating behavior (implicit semantics). I will describe the different variants of the semantically-enhanced pre-filtering approach that we have developed and evaluated. Finally, I will talk about the experimental results of our performance comparison using several contextually-tagged ratings data sets, showing that the proposed pre-filtering approach outperforms two state-of-the-art context-aware recommendation techniques.