Personalized Game Difficulty Prediction Using Factorization Machines
This work addresses the problem of optimizing user experience in games through personalized difficulty prediction, but it is incremental as it adapts existing recommendation methods to a new domain.
The paper tackled personalized difficulty estimation for game levels by applying factorization machines to a large commercial puzzle game dataset, achieving promising results compared to non-personalized and personalized baselines like Random Forests.
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.