Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study

TL;DR

This study applies language models to interpret game event sequences as a specialized language, enabling better understanding of player behavior and segmentation for improved game design and personalization.

Contribution

It introduces a novel approach of using language models on game event data, capturing player interactions without needing labeled data.

Findings

Language models effectively identify meaningful player segments.

Self-supervised learning enhances game personalization.

Transforming game events into text improves analysis accuracy.

Abstract

This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our approach captures the rich and nuanced interactions within game sessions, effectively identifying meaningful player segments. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels.