Personalized Game Difficulty Prediction Using Factorization Machines

TL;DR

This paper introduces a personalized difficulty prediction method for game levels using factorization machines, which improves accuracy and offers insights into player and level characteristics.

Contribution

The paper presents a novel application of factorization machines for personalized game difficulty estimation, demonstrating scalability and interpretability in a large dataset.

Findings

Factorization machines outperform non-personalized baselines.

FMs provide insights into player and level features affecting difficulty.

The approach is scalable for large datasets.

Abstract

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.