When recommendations fail, trust in a recommender system often decreases, particularly when the system acts like a "black box". To deal with this issue, it is important to support exploration of recommendations by explicitly exposing relationships that can provide explanations. As an example, a graph-based visualization can help to explain collaborative filtering results by representing relationships among items and users. In our work, we focus on the use of visualization techniques to support exploration of multiple relevance prospects - such as relationships between different recommendation methods, socially connected users and tags. More specifically, we researched how users explore relationships between such multiple relevance prospects with two set-based visualization techniques: a clustermap and a Venn diagram. A comparative analysis of user studies with these two approaches indicates that, although effectiveness of recommendations increases with the use of a clustermap, the approach is too complex for a non-technical audience. A Venn diagram representation is more intuitive and users are more likely to explore relationships that help them find relevant items
