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arXiv · 2601.05257

KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits

Hou-Wan Long·Yicheng Song·Zidong Wang·Tianshu Sun
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Abstract

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.

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Hou-Wan Long, Yicheng Song, Zidong Wang, Tianshu Sun. 2025-10-20. KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits. https://arxiv.org/abs/2601.05257

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