  Under the assumptions that (i) gamification consists of various types of
users that experience game design elements differently; and (ii) gamification
is deployed in order to achieve some goal in the broadest sense, we pose the
gamification problem as that of assigning each user a game design element that
maximizes their expected contribution in order to achieve that goal. We show
that this problem reduces to a statistical learning problem and suggest matrix
factorization as one solution when user interaction data is given. The
hypothesis is that predictive models as intelligent tools for supporting users
in decision-making may also have potential to support the design process in
gamification.
