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

Tianze Wang; Maryam Honari-Jahromi; Styliani Katsarou; Olga Mikheeva; Theodoros Panagiotakopoulos; Oleg Smirnov; Lele Cao; Sahar Asadi

arXiv · 2410.18605

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

Tianze Wang·Maryam Honari-Jahromi·Styliani Katsarou·Olga Mikheeva·Theodoros Panagiotakopoulos·Oleg Smirnov·Lele Cao·Sahar Asadi

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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.

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Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou, Olga Mikheeva, Theodoros Panagiotakopoulos, Oleg Smirnov, Lele Cao, Sahar Asadi. 2024-10-24. Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study. https://arxiv.org/abs/2410.18605

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