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

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

Machine Learning 2024-10-25 v1

Authors: 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.

Keywords

language models game event sequences customized natural language Longformer player segmentation self-supervised learning game personalization mobile game

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@article{arxiv.2410.18605,
  title  = {Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study},
  author = {Tianze Wang and Maryam Honari-Jahromi and Styliani Katsarou and Olga Mikheeva and Theodoros Panagiotakopoulos and Oleg Smirnov and Lele Cao and Sahar Asadi},
  journal= {arXiv preprint arXiv:2410.18605},
  year   = {2024}
}

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published in Workshop on Customizable NLP at EMNLP 2024

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

Abstract

Keywords

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