  Recommender systems relying on latent factor models often appear as black
boxes to their users. Semantic descriptions for the factors might help to
mitigate this problem. Achieving this automatically is, however, a
non-straightforward task due to the models' statistical nature. We present an
output-agreement game that represents factors by means of sample items and
motivates players to create such descriptions. A user study shows that the
collected output actually reflects real-world characteristics of the factors.
