KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911362061434880 |
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| author | Long, Hou-Wan Song, Yicheng Wang, Zidong Sun, Tianshu |
| author_facet | Long, Hou-Wan Song, Yicheng Wang, Zidong Sun, Tianshu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits Long, Hou-Wan Song, Yicheng Wang, Zidong Sun, Tianshu Information Retrieval Artificial Intelligence 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. |
| title | KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2601.05257 |