用智能代理自动优化用户价值预测模型，省去人工调参。
AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction
- 用代码化管道+智能代理自动搜索最优预测方案。
- 在多个数据集上超越传统方法，尤其提升高价值用户预测精度。
- 适合需要快速适配新场景的工业级推荐系统团队。
用户生命周期价值（LTV）预测在广告、推荐系统和电商中至关重要。实际应用中，不同决策场景下的LTV数据模式差异显著，导致从业者需构建复杂且场景特定的建模流程，频繁迭代特征处理、目标设计与参数调优，成本高且难以迁移。为此，本文提出AgentLTV——一种基于智能体的统一搜索与演化框架，用于自动化LTV建模。该框架将每个候选方案视为可执行的管道程序，由大模型驱动的智能体生成代码、运行并修复管道，分析执行反馈。两个决策智能体协同完成两阶段搜索：第一阶段采用蒙特卡洛树搜索（MCTS），在固定预算下探索广泛建模选择，基于多项式上限置信区间准则与帕累托感知多指标价值函数引导；第二阶段通过岛屿演化算法对最佳MCTS程序进行精细化优化，包含交叉、变异与迁移操作。在大规模私有数据集及公开基准上的实验表明，AgentLTV在排序与误差指标上持续表现优异。在线桶级分析进一步显示其提升了排名一致性与价值校准能力，尤其在高价值与负LTV群体中效果显著。我们总结出实用建议：用MCTS快速适应新数据模式，用EA实现稳定优化，并通过桶级排名与校准诊断验证部署可行性。AgentLTV已成功上线部署。
原文摘要 · Abstract (English)
Lifetime Value (LTV) prediction is critical in advertising, recommender systems, and e-commerce. In practice, LTV data patterns vary across decision scenarios. As a result, practitioners often build complex, scenario-specific pipelines and iterate over feature processing, objective design, and tuning. This process is expensive and hard to transfer. We propose AgentLTV, an agent-based unified search-and-evolution framework for automated LTV modeling. AgentLTV treats each candidate solution as an {executable pipeline program}. LLM-driven agents generate code, run and repair pipelines, and analyze execution feedback. Two decision agents coordinate a two-stage search. The Monte Carlo Tree Search (MCTS) stage explores a broad space of modeling choices under a fixed budget, guided by the Polynomial Upper Confidence bounds for Trees criterion and a Pareto-aware multi-metric value function. The Evolutionary Algorithm (EA) stage refines the best MCTS program via island-based evolution with crossover, mutation, and migration. Experiments on a large-scale proprietary dataset and a public benchmark show that AgentLTV consistently discovers strong models across ranking and error metrics. Online bucket-level analysis further indicates improved ranking consistency and value calibration, especially for high-value and negative-LTV segments. We summarize practitioner-oriented takeaways: use MCTS for rapid adaptation to new data patterns, use EA for stable refinement, and validate deployment readiness with bucket-level ranking and calibration diagnostics. The proposed AgentLTV has been successfully deployed online.
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