Traditionally, content-based news recommendation is performed by means of the cosine similarity and the TF-IDF weighting scheme for terms occurring in news messages and user profiles. Semanticsdriven variants like SF-IDF additionally take into account term meaning by exploiting synsets from semantic lexicons. However, semantics-based weighting techniques are not able to handle-often crucial-named entities, which are often not present in semantic lexicons. Hence, we extend SF-IDF by also employing named entity similarities using Bing page counts. Our proposed method, Bing-SF-IDF, outperforms TF-IDF and its semantics-driven variants in terms of F<inf>1</inf>-scores and kappa statistics.