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

2026-09-16

AI Summary

为解决多阶段架构导致的目标不一致及现有方法泛化能力差的问题，提出ANGLE框架，利用层级文本表示和单一LLM集成检索、相关性和排序功能，提高广告检索效率与商业价值评估。

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.

Problem

• multi-stage cascading architectures
• semantic identifiers
• large language model
• retrieval efficiency
• commercial value assessment

Innovation

• Hierarchical Text Representations
• Unified Framework
• Real-Time Retrieval
• LLM
• Efficient Ranking