通过短周期辅助提升长期用户价值预测准确率
SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
- 用短期预测任务辅助长期价值建模，缓解数据稀疏问题
- 混合损失函数有效降低零充值和高价值异常值影响
- 在真实游戏数据上相对误差降低47.91%，适合工业级应用
在数字游戏领域，长期用户生命周期价值（LTV）预测对商业化策略至关重要，但受制于支付行为延迟、早期用户数据稀疏及高价值异常值等挑战。现有模型多依赖短期观测或强分布假设，常导致长期价值低估或鲁棒性差。为此，我们提出SHORE（Short-cycle auxiliary with Order-preserving REgression）框架，将短期预测（如LTV-15、LTV-30）作为辅助任务，以增强长期目标（如LTV-60）的建模效果。同时引入融合顺序保持多分类与动态Huber损失的混合损失函数，有效缓解零充值和异常支付行为的影响。在真实世界数据集上的离线与在线实验表明，SHORE显著优于现有基线，在线上部署中实现47.91%的相对误差降低，展现出优异的实用性与鲁棒性。
原文摘要 · Abstract (English)
In digital gaming, long-term user lifetime value (LTV) prediction is essential for monetization strategy, yet presents major challenges due to delayed payment behavior, sparse early user data, and the presence of high-value outliers. While existing models typically rely on either short-cycle observations or strong distributional assumptions, such approaches often underestimate long-term value or suffer from poor robustness. To address these issues, we propose SHort-cycle auxiliary with Order-preserving REgression (SHORE), a novel LTV prediction framework that integrates short-horizon predictions (e.g., LTV-15 and LTV-30) as auxiliary tasks to enhance long-cycle targets (e.g., LTV-60). SHORE also introduces a hybrid loss function combining order-preserving multi-class classification and a dynamic Huber loss to mitigate the influence of zero-inflation and outlier payment behavior. Extensive offline and online experiments on real-world datasets demonstrate that SHORE significantly outperforms existing baselines, achieving a 47.91\% relative reduction in prediction error in online deployment. These results highlight SHORE's practical effectiveness and robustness in industrial-scale LTV prediction for digital games.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。