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arXiv · 2609.18296

One-Step Retrieval Framework for Real-Time Sponsored Search Ads Using Hierarchical Text Representations

Tongtong Liu·Renyu Zhang·Jiayu Ding·Hongchao Guo·Xintao Yang·He Wei·Zhaoyu Li·Haiyang Wu
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Abstract

Traditional retrieval systems typically use multi-stage cascading architectures (MCA), where each module is optimized independently, leading to inconsistent objectives and the premature elimination of high-potential candidates. Recent LLM-based generation methods offer end-to-end solutions but use discrete semantic identifiers (SIDs) to retrieve ads, which are not learned by the base LLM and require memorization of numerous SID-to-ad mappings during SFT, suffering from limited generalization to unseen ads, high maintenance and update costs. The one-to-one mapping between SIDs and advertisements leads to inefficient decoding. Moreover, these methods rely on a small reward model (e.g. pctr) for relevance and ranking, limiting the LLM's ability to fully assess ads' commercial value. To address these challenges, we propose A uNified Generation-discriminative-ranking reaL-time rEtrieval (ANGLE) framework. ANGLE uses LLM-generated hierarchical textual representations, which consist of commercial intent that provide high-level overviews and ad abstract that deliver fine-grained details. Additionally, ANGLE integrates retrieval, relevance, and ranking directly within a single LLM, enabling precise and efficient ranking of ads by leveraging the full capabilities of the LLM. We applied ANGLE to the real-world search scenarios, achieving a 1.81% increase in consumption and a 2.16% increase in gross merchandise volume (GMV). We also conducted offline evaluations of ANGLE and seven baselines, with ANGLE outperforming all across key metrics such as HR and ACR.

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Tongtong Liu, Renyu Zhang, Jiayu Ding, Hongchao Guo, Xintao Yang, He Wei, Zhaoyu Li, Haiyang Wu. 2026-09-16. One-Step Retrieval Framework for Real-Time Sponsored Search Ads Using Hierarchical Text Representations. https://arxiv.org/abs/2609.18296

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