Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
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.
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Methods
AdamW •
Attention Dropout •
Dense Connections •
Dilated Sliding Window Attention •
Dropout •
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GELU •
Global and Sliding Window Attention •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Longformer •
Multi-Head Attention •
Residual Connection •
Sliding Window Attention •
Softmax •
Weight Decay •
WordPiece
