用大模型强化学习优化广告关键词清理，提升49%收益
KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits
- 基于大模型与上下文老虎机框架，自动生成关键词优化代码
- 在真实药企广告数据上实现最高49.28%的累计利润提升
- 适合广告投放优化、智能营销系统研发人员参考
赞助式搜索广告（SSA）要求广告主持续调整关键词策略。尽管出价调整和关键词生成已有深入研究，但关键词清理——即优化关键词集合以提升广告表现——仍缺乏足够探索。本文基于来自中国最大配送平台美团的一家医药广告商的0.5百万条真实广告记录，揭示了现有实践中的关键低效问题。提出KP-Agent，一个结合领域工具集与记忆模块的大模型代理系统。通过将关键词清理建模为上下文老虎机问题，该系统利用强化学习生成代码片段，动态优化关键词集合。实验表明，相较于基线方法，KP-Agent可使累计利润提升高达49.28%。
原文摘要 · Abstract (English)
Sponsored search advertising (SSA) requires advertisers to constantly adjust keyword strategies. While bid adjustment and keyword generation are well-studied, keyword pruning-refining keyword sets to enhance campaign performance-remains under-explored. This paper addresses critical inefficiencies in current practices as evidenced by a dataset containing 0.5 million SSA records from a pharmaceutical advertiser on search engine Meituan, China's largest delivery platform. We propose KP-Agent, an LLM agentic system with domain tool set and a memory module. By modeling keyword pruning within a contextual bandit framework, KP-Agent generates code snippets to refine keyword sets through reinforcement learning. Experiments show KP-Agent improves cumulative profit by up to 49.28% over baselines.
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。