# Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising

arXiv: https://arxiv.org/abs/2504.01304 · HTML: https://mlsanity.com/paper/2504.01304/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2025.

## Built on by (most cited first, 8 of 8)

- [Look It Up: Analysing Internal Web Search Capabilities of Modern LLMs](https://mlsanity.com/paper/2511.18931/) (2025)
- [MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution](https://mlsanity.com/paper/2512.05958/) (2025)
- [Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent](https://mlsanity.com/paper/2601.04879/) (2026)
- [It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning](https://mlsanity.com/paper/2609.00638/) (2026)
- [One Model, Two Markets: Bid-Aware Generative Recommendation](https://mlsanity.com/paper/2603.22231/) (2026)
- [PromptPack: Scaling LLM Annotation Agents for Online Recommendation](https://mlsanity.com/paper/2607.20528/) (2026)
- [UniPolicy: Unified Objective-Specific Policies for Generative Search Advertising](https://mlsanity.com/paper/2609.20630/) (2026)
- [From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents](https://mlsanity.com/paper/2610.09039/) (2026)

## Abstract (arXiv)

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.