research-article

Personalized Game Difficulty Prediction Using Factorization Machines

Authors:

Jeppe Theiss Kristensen,

Christian Guckelsberger,

Perttu HämäläinenAuthors Info & Claims

UIST '22: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology

Article No.: 88, Pages 1 - 13

https://doi.org/10.1145/3526113.3545624

Published: 28 October 2022 Publication History

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.

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Cited By

Kristensen JBurelli P(2024)Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated DataInternational Journal of Computer Games Technology10.1155/2024/55923732024:1Online publication date: 26-Jun-2024
https://doi.org/10.1155/2024/5592373
Guo ZThawonmas RRen X(2024)Rethinking dynamic difficulty adjustment for video game designEntertainment Computing10.1016/j.entcom.2024.10066350(100663)Online publication date: May-2024
https://doi.org/10.1016/j.entcom.2024.100663

Index Terms

Personalized Game Difficulty Prediction Using Factorization Machines
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models

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cover image ACM Conferences

UIST '22: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology

October 2022

1363 pages

ISBN:9781450393201

DOI:10.1145/3526113

Editors:
Maneesh Agrawala
Stanford University, USA
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Jacob O. Wobbrock
University of Washington, USA
,
Eytan Adar
University of Michigan, USA
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Vidya Setlur
Tableau Research, USA

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Published: 28 October 2022

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Cited By

Kristensen JBurelli P(2024)Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated DataInternational Journal of Computer Games Technology10.1155/2024/55923732024:1Online publication date: 26-Jun-2024
https://doi.org/10.1155/2024/5592373
Guo ZThawonmas RRen X(2024)Rethinking dynamic difficulty adjustment for video game designEntertainment Computing10.1016/j.entcom.2024.10066350(100663)Online publication date: May-2024
https://doi.org/10.1016/j.entcom.2024.100663

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