  We show a prototype of a system that uses multiwinner voting to suggest
resources (such as movies) related to a given query set (such as a movie that
one enjoys). Depending on the voting rule used, the system can either provide
resources very closely related to the query set or a broader spectrum of
options. We show how this ability can be interpreted as a way of controlling
the diversity of the results. We test our system both on synthetic data and on
the real-life collection of movie ratings from the MovieLens dataset. We also
present a visual comparison of the search results corresponding to selected
diversity levels.
