  The accurate and personalized estimation of task difficulty provides many
opportunities for optimizing user experience. However, user diversity makes
such difficulty estimation hard, in that empirical measurements from some user
sample do not necessarily generalize to others. In this paper, we contribute a
new approach for personalized difficulty estimation of game levels, borrowing
methods from content recommendation. Using factorization machines (FM) on a
large dataset from a commercial puzzle game, we are able to predict difficulty
as the number of attempts a player requires to pass future game levels, based
on observed attempt counts from earlier levels and levels played by others. In
addition to performance and scalability, FMs offer the benefit that the learned
latent variable model can be used to study the characteristics of both players
and game levels that contribute to difficulty. We compare the approach to a
simple non-personalized baseline and a personalized prediction using Random
Forests. Our results suggest that FMs are a promising tool enabling game
designers to both optimize player experience and learn more about their players
and the game.
