用大模型让电商定价更智能，兼顾长期收益与可解释性。
AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing
- 用大模型结合领域知识与文本上下文生成可解释的定价策略。
- 14天内提升GMV 13.21%、ROI 7.59%、里程碑达成率8.20%。
- 适合关注长期商业目标与决策透明度的电商系统优化者。
大规模电商中的传统动态定价模型存在可解释性差、未结构化信息利用不足，以及与长期业务目标（如累计商品交易额GMV、投资回报率ROI和里程碑达成）不一致的问题。我们提出AIGP框架，通过提示领域知识、结构化数据与文本上下文的大语言模型（LLM），实现可解释且具备知识感知的定价决策。为高效部署并保持高质量输出，采用监督微调进行知识蒸馏。AIGP的核心是离线强化学习训练的长期价值估计算器（LTVE），作为奖励模型对候选定价动作评分，并用于选择偏好对进行直接偏好优化（DPO），从而对齐定价策略与长期业务目标。在淘宝工厂平台的大量离线评估与大规模在线A/B测试表明，相较于生产基线，AIGP在14天内实现GMV提升13.21%、ROI提升7.59%、里程碑达成率提升8.20%，同时提供可解释的定价理由。
原文摘要 · Abstract (English)
Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement. We propose AIGP, a novel framework that leverages a Large Language Model (LLM) prompted with domain knowledge, structured data and textual context to make interpretable, knowledge-aware pricing decisions. For efficient deployment while maintaining high-quality outputs, we employ supervised fine-tuning for knowledge distillation. Central to AIGP is the Long-Term Value Estimator (LTVE), trained via offline reinforcement learning on historical data, which serves as a reward model to score candidate pricing actions and select preference pairs for Direct Preference Optimization (DPO), thereby aligning the pricing policy with long-term business objectives. Extensive offline evaluations and large-scale online A/B tests on Tao Factory demonstrate that AIGP achieves significant improvements: +13.21% in GMV, +7.59% in ROI, and +8.20% in milestone achievement rate over 14 days compared to the production baseline, while simultaneously providing interpretable and transparent pricing rationales.
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