Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912305536565248 |
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| author | Liu, Tongtong Wang, Zhaohui Qin, Meiyue Lu, Zenghui Chen, Xudong Yang, Yuekui Shu, Peng |
| author_facet | Liu, Tongtong Wang, Zhaohui Qin, Meiyue Lu, Zenghui Chen, Xudong Yang, Yuekui Shu, Peng |
| contents | The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits scalability in large-scale corpora. In this paper, we propose the Real-time Ad REtrieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time. These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance. Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs. RARE has been implemented in a real-world online system, handling daily search volumes in the hundreds of millions. The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions. Extensive offline experiments show RARE's superiority over ten competitive baselines in four major categories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01304 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising Liu, Tongtong Wang, Zhaohui Qin, Meiyue Lu, Zenghui Chen, Xudong Yang, Yuekui Shu, Peng Information Retrieval The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits scalability in large-scale corpora. In this paper, we propose the Real-time Ad REtrieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time. These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance. Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs. RARE has been implemented in a real-world online system, handling daily search volumes in the hundreds of millions. The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions. Extensive offline experiments show RARE's superiority over ten competitive baselines in four major categories. |
| title | Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2504.01304 |