Recommender systems provide a valuable mechanism to address the information overload problem by reducing a data set to the items that may be interesting for a particular user. While the quality of recommendations has notably improved in the recent years, the complex algorithms in use lead to high non-transparency for the end user. We propose the usage of interactive visualizations for presenting recommendations. By involving the user in the information reduction process, the quality of recommendations could be enhanced whilst keeping the system’s transparency. This work gives first insights by analyzing recommender systems data and matching them to suitable visualization and interaction techniques. The findings are illustrated by means of an example scenario based on a typical real-world setting.