With the surge in mobile gaming, accurately predicting user spending on newly
downloaded games has become paramount for maximizing revenue. However, the
inherently unpredictable nature of user behavior poses significant challenges
in this endeavor. To address this, we propose a robust model training and
evaluation framework aimed at standardizing spending data to mitigate label
variance and extremes, ensuring stability in the modeling process. Within this
framework, we introduce a collaborative-enhanced model designed to predict user
game spending without relying on user IDs, thus ensuring user privacy and
enabling seamless online training. Our model adopts a unique approach by
separately representing user preferences and game features before merging them
as input to the spending prediction module. Through rigorous experimentation,
our approach demonstrates notable improvements over production models,
achieving a remarkable \textbf{17.11}\% enhancement on offline data and an
impressive \textbf{50.65}\% boost in an online A/B test. In summary, our
contributions underscore the importance of stable model training frameworks and
the efficacy of collaborative-enhanced models in predicting user spending
behavior in mobile gaming.

通过提出一个稳健的模型训练和评估框架，以标准化支出数据以降低标签变异性和异常值，并确保建模过程的稳定性，该论文介绍了一种协同增强模型，旨在预测用户游戏消费而无需依赖用户标识，从而确保用户隐私并实现在线训练，通过在融合用户偏好和游戏特征之前分别表示它们并将其作为支出预测模块的输入，通过严格的实验，证明了我们的方法在离线数据上实现了显著的 17.11% 的改进，并在在线 A/B 测试中获得了令人瞩目的 50.65% 的提升，总结我们的贡献突显了稳定的模型训练框架的重要性以及协同增强模型在移动游戏中预测用户消费行为方面的功效。