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

arXiv: https://arxiv.org/abs/2410.18605 · HTML: https://mlsanity.com/paper/2410.18605/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Using Temporal Data for Making Recommendations](https://mlsanity.com/paper/1301.2320/) (2013)
- [Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games](https://mlsanity.com/paper/2007.15236/) (2020)
- [Automating Gamification Personalization: To the User and Beyond](https://mlsanity.com/paper/2101.05718/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects](https://mlsanity.com/paper/2502.04899/) (2025)

## 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.