Enregistré dans:
Détails bibliographiques
Auteurs principaux: Wang, Tianze, Honari-Jahromi, Maryam, Katsarou, Styliani, Mikheeva, Olga, Panagiotakopoulos, Theodoros, Smirnov, Oleg, Cao, Lele, Asadi, Sahar
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2410.18605
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Table des matières:
  • 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.