拆解用户付费行为，更准预测手游玩家长期价值。
Mobile Gamer Lifetime Value Prediction via Objective Decomposition and Reconstruction
- 将付费预测分解为交易次数与价格的中间步骤，提升建模精度。
- 在真实工业数据上表现优于现有最优模型（ZILN），A/B测试验证有效。
- 适合需精准广告投放的移动游戏平台和实时竞价系统使用。
对于提供实时竞价（RTB）广告服务的互联网平台而言，全面理解用户生命周期价值（LTV）对优化广告分配效率、最大化广告主投资回报率（ROI）至关重要，有助于推动平台商业化收入增长。然而，用户LTV分布的固有复杂性给精准预测带来显著挑战。现有前沿方法主要通过精心设计的损失函数直接学习LTV分布，但因对异常值敏感，效果有限。本文提出一种新型LTV预测方法，通过目标分解与重构框架应对分布难题。具体而言，基于移动端游戏玩家的内购特征，模型首先预测特定价格下的交易次数，再由这些中间预测结果计算总支付金额。该模型在真实工业数据集上进行实验评估，并部署于TapTap RTB广告系统，与当前最优的ZILN模型进行在线A/B测试，验证了其有效性。
原文摘要 · Abstract (English)
For Internet platforms operating real-time bidding (RTB) advertising service, a comprehensive understanding of user lifetime value (LTV) plays a pivotal role in optimizing advertisement allocation efficiency and maximizing the return on investment (ROI) for advertisement sponsors, thereby facilitating growth of commercialization revenue for the platform. However, the inherent complexity of user LTV distributions induces significant challenges in accurate LTV prediction. Existing state-of-the-art works, which primarily focus on directly learning the LTV distributions through well-designed loss functions, achieve limited success due to their vulnerability to outliers. In this paper, we proposed a novel LTV prediction method to address distribution challenges through an objective decomposition and reconstruction framework. Briefly speaking, based on the in-app purchase characteristics of mobile gamers, our model was designed to first predict the number of transactions at specific prices and then calculate the total payment amount from these intermediate predictions. Our proposed model was evaluated through experiments on real-world industrial dataset, and deployed on the TapTap RTB advertising system for online A/B testing along with the state-of-the-art ZILN model.
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