Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 24 Oct 2024.
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12 of 30 references.
References · 12
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