用蒙特卡洛丢弃法预测用户价值，同时给出可信度评估。
Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout
- 在神经网络中引入蒙特卡洛丢弃，捕捉模型不确定性。
- 在头部5%用户上误差降低，优于现有最先进方法。
- 输出结果含置信度，帮助企业做更精准决策。
准确预测客户生命周期价值（LTV）对优化企业收入策略至关重要。传统深度学习模型虽有效，但通常仅提供点估计，无法捕捉用户行为建模中的模型不确定性。为此，我们提出一种新方法，通过在纯神经网络架构中引入蒙特卡洛丢弃（Monte Carlo Dropout, MCD）框架进行增强。我们在全球下载量最高的移动端游戏之一的数据上进行了基准测试，结果显示，相比现有最先进方法，该方法在预测的前5%用户上的平均绝对百分比误差显著降低。此外，该方法还提供了置信度指标，作为评估各类神经网络模型性能的额外维度，有助于企业做出更明智的决策。
原文摘要 · Abstract (English)
Accurately predicting customer Lifetime Value (LTV) is crucial for companies to optimize their revenue strategies. Traditional deep learning models for LTV prediction are effective but typically provide only point estimates and fail to capture model uncertainty in modeling user behaviors. To address this limitation, we propose a novel approach that enhances the architecture of purely neural network models by incorporating the Monte Carlo Dropout (MCD) framework. We benchmarked the proposed method using data from one of the most downloaded mobile games in the world, and demonstrated a substantial improvement in predictive Top 5\% Mean Absolute Percentage Error compared to existing state-of-the-art methods. Additionally, our approach provides confidence metric as an extra dimension for performance evaluation across various neural network models, facilitating more informed business decisions.
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