In the last years, there has been much a ention given to the semantic gap problem in multimedia retrieval systems. Much e ort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it.In this paper, we explore a di erent point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract lowlevel Mise-en-Scène features from multimedia content and combine it with high-level features provided by the wisdom of the crowd.To this end, we rst performed an o ine performance assessment by implementing a pure content-based recommender system with three di erent versions of the same algorithm, respectively based on (i) conventional movie a ributes, (ii) mise-en-scène features, and (iii) a hybrid method that interleaves recommendations based on movie a ributes and mise-en-scène features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scène features in conjunction with traditional movie a ributes improves both o ine and online quality of recommendations.