Personalized Game Difficulty Prediction Using Factorization Machines

Kristensen, Jeppe Theiss; Guckelsberger, Christian; Burelli, Paolo; Hämäläinen, Perttu

doi:10.1145/3526113.3545624

Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology 2022

DOI: 10.1145/3526113.3545624

|View full text |Cite

Personalized Game Difficulty Prediction Using Factorization Machines

Jeppe Theiss Kristensen

Christian Guckelsberger

Paolo Burelli

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Rethinking dynamic difficulty adjustment for video game design

Guo,

Thawonmas,

Ren

2024

Entertainment Computing

View full text Add to dashboard Cite

Rethinking dynamic difficulty adjustment for video game design

Guo,

Thawonmas,

Ren

2024

Entertainment Computing

View full text Add to dashboard Cite

Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data

Kristensen,

Burelli

2024

International Journal of Computer Games Technology

View full text Add to dashboard Cite

Difficulty is one of the key drivers of player engagement, and it is often one of the aspects that designers tweak most to optimise the player experience; operationalising it is, therefore, a crucial task for game development studios. A common practice consists of creating metrics out of data collected by player interactions with the content; however, this allows for estimation only after the content is released and does not consider the characteristics of potential future players. In this article, we present a number of potential solutions for the estimation of difficulty under such conditions, and we showcase the results of a comparative study intended to understand which method and which types of data perform better in different scenarios. The results reveal that models trained on a combination of cohort statistics and simulated data produce the most accurate estimations of difficulty in all scenarios. Furthermore, among these models, artificial neural networks show the most consistent results.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Personalized Game Difficulty Prediction Using Factorization Machines

Cited by 2 publications

References 39 publications

Rethinking dynamic difficulty adjustment for video game design

Rethinking dynamic difficulty adjustment for video game design

Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data

Contact Info

Product

Resources

About