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AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction

Chaowei Wu, Huazhu Chen, Congde Yuan, Qirui Yang, Guoqing Song, Yue Gao, Li Luo, Frank Youhua Chen, Mengzhuo Guo

Abstract

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.

AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction

Abstract

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.
Paper Structure (28 sections, 7 equations, 4 figures, 6 tables, 3 algorithms)

This paper contains 28 sections, 7 equations, 4 figures, 6 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Lifetime Value Predictions
  4. Code Generation with LLMs
  5. Problem Definition
  6. Framework of AgentLTV
  7. Configuration Module
  8. Monte Carlo Tree Search Module
  9. Evolutionary Algorithm Module
  10. Experiment
  11. Experimental Setup
  12. Dataset
  13. Metrics
  14. Baselines
  15. Implementation Details
  16. ...and 13 more sections

Figures (4)

  • Figure 1: The current challenges faced by the LTV prediction scene.
  • Figure 2: AgentLTV overview. (a) Configuration module instantiates LLM-driven agents and execution interfaces. (b) MCTS module performs broad exploration over LTV pipeline programs via PUCT-guided expansion after the Code Generator generates the initial node algorithm. (c) EA module refines the best MCTS program using island-based evolution with crossover, mutation, and migration based on the previous optimal algorithm.
  • Figure 3: Monte Carlo tree search Module visualization.
  • Figure 4: Online economic analysis on bucketed LTV values. (a) Same Interval Ratio: the fraction of users whose predicted LTV falls into the same value bucket as the bucket defined by the ground-truth LTV ranking. Higher is better and indicates stronger ranking consistency across buckets. (b) Mean Absolute Error (MAE) within each bucket: the absolute prediction error averaged over users in that bucket. Lower is better and reflects better value calibration.