用大模型打造实时竞价智能体，提升广告投放盈利
RTBAgent: A LLM-based Agent System for Real-Time Bidding
- 基于大模型构建决策系统，融合点击率预估与专家策略
- 两阶段决策+多记忆检索，实时适应市场波动
- 在真实数据上显著提升收益，适合广告算法研发者
实时竞价（RTB）使广告商能即时竞标展示机会，在激烈竞争中追求成本效益。尽管深度学习与强化学习已广泛应用于RTB，但线上与线下环境差异及竞价快速波动常导致方法可靠性下降。为此，本文提出首个基于大语言模型（LLM）的RTB智能体系统RTBAgent，通过整合推理能力与实时竞价环境同步，实现动态定价。具体地，利用LLM获得推理能力，并引入点击率估计模型、专家策略知识库和每日反思模块，增强专业性；设计两阶段决策流程与多记忆检索机制，使系统可回溯历史决策与交易记录，从而更灵活应对市场变化。基于真实广告数据集的实证测试表明，RTBAgent显著提升了盈利能力。代码将公开于：https://github.com/CaiLeng/RTBAgent。
原文摘要 · Abstract (English)
Real-Time Bidding (RTB) enables advertisers to place competitive bids on impression opportunities instantaneously, striving for cost-effectiveness in a highly competitive landscape. Although RTB has widely benefited from the utilization of technologies such as deep learning and reinforcement learning, the reliability of related methods often encounters challenges due to the discrepancies between online and offline environments and the rapid fluctuations of online bidding. To handle these challenges, RTBAgent is proposed as the first RTB agent system based on large language models (LLMs), which synchronizes real competitive advertising bidding environments and obtains bidding prices through an integrated decision-making process. Specifically, obtaining reasoning ability through LLMs, RTBAgent is further tailored to be more professional for RTB via involved auxiliary modules, i.e., click-through rate estimation model, expert strategy knowledge, and daily reflection. In addition, we propose a two-step decision-making process and multi-memory retrieval mechanism, which enables RTBAgent to review historical decisions and transaction records and subsequently make decisions more adaptive to market changes in real-time bidding. Empirical testing with real advertising datasets demonstrates that RTBAgent significantly enhances profitability. The RTBAgent code will be publicly accessible at: https://github.com/CaiLeng/RTBAgent.
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