Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
This is an incremental approach for game developers to improve personalization using self-supervised language models.
This pilot study tackled the problem of modeling player interactions in games by treating game event sequences as a custom language, using a Longformer model to identify meaningful player segments without ground-truth labels, demonstrating potential for enhancing game design and personalization.
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.