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DPBL: Denoised Player Behavior Representation Learning
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AI
DOI：10.1109/TG.2025.3601181.png)
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Abstract
En 中文
The video game industry has emerged as a significant economic force, driving extensive research on optimizing the gaming environment and improving gaming experiences. Among these endeavors, player behavior representation learning has become a critical way to model valuable player properties and is beneficial for a wide range of downstream tasks. However, some common factors, such as login rewards and daily tasks, can trigger similar behaviors among different players, which are informative and noisy for learning high-quality player behavior representations. Existing methods ignore the low signal-to-noise ratio in player behavior data and waste too much modeling capacity on less informative behaviors, resulting in their learned representations being noisy. In this article, we propose a novel model for denoised player behavior representation learning (DPBL), which consists of two key modules. The first module extracts various player behavior patterns and isolates them from less informative noise. The second module utilizes the extracted patterns to refine the embedding of each behavior and eliminates noise. To optimize DPBL, two contrastive learning strategies are proposed to identify the noise that should be eliminated and to learn distinguishable representations, respectively. With the above design, DPBL is capable of mitigating the impact of noise in the data and learning high-quality representations that effectively capture player characteristics. We conducted extensive experiments on two real-world datasets, and DPBL outperforms all baselines on various downstream tasks with an improvement of 1.4%-18.1%. The results also show that DPBL achieves an improvement of 5.6%-23.0% in the denoising experiments, which proves that DPBL is more robust to noisy behaviors.
Keywords:
Games
Noise
Representation learning
Noise measurement
Churn
Chatbots
Data models
Data mining
Predictive models
Industries
unsupervised learning
user modeling
Journal
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2.8
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45
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0