用于实时赞助搜索广告的一步检索框架：基于层次化文本表示

中文摘要 AI 辅助

传统检索系统通常采用多级级联架构（MCA），其中每个模块独立优化，导致目标不一致，并过早淘汰高潜力候选。近期基于大语言模型（LLM）的生成方法提供了端到端解决方案，但使用离散语义标识符（SID）来检索广告，这些标识符并非由基础LLM学习得到，且在监督微调（SFT）期间需要记忆大量SID到广告的映射，导致对未见广告的泛化能力有限，维护和更新成本高。SID与广告之间的一一映射导致解码效率低下。此外，这些方法依赖小型奖励模型（如pctr）进行相关性和排序，限制了LLM全面评估广告商业价值的能力。为解决这些挑战，我们提出了统一生成-判别-排序的实时检索（ANGLE）框架。ANGLE使用LLM生成的层次化文本表示，包括提供高层概览的商业意图和提供细粒度细节的广告摘要。此外，ANGLE将检索、相关性和排序直接集成在单个LLM中，通过利用LLM的全部能力实现精确高效的广告排序。我们将ANGLE应用于真实搜索场景，实现了消费增长1.81%和总商品交易额（GMV）增长2.16%。我们还对ANGLE和七个基线进行了离线评估，ANGLE在HR和ACR等关键指标上均优于所有基线。

英文摘要

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.

发表机构

• Tencent Inc.（腾讯公司）

机构由 AI 辅助整理，请以论文原文为准。