用大模型提升电商搜索相关性，兼顾内容与用户行为。
Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study
- 拆分相关性为内容与互动两类，用大模型生成训练标签和特征。
- 通过sigmoid变换强化评分差异，更好平衡两类相关性。
- 实测证明能更准确排序高相关商品，适合电商搜索优化者。
电商搜索排序的训练因缺乏明确的相关性标准而困难。本文将相关性分解为内容相关与用户行为相关两方面，提出利用大语言模型（LLMs）在训练中生成标签与特征，主要提升对内容相关性的预测能力。同时，引入不同的sigmoid变换对大模型输出进行极化处理，增强模型平衡内容与行为相关性的能力，从而更优地优先展示高度相关商品。本文还进行了全面的线上测试与离线评估。结果表明，该设计为将大模型融入电商搜索排序训练提供了有效策略，推动了更高效、更均衡的排序模型发展。
原文摘要 · Abstract (English)
Training Learning-to-Rank models for e-commerce product search ranking can be challenging due to the lack of a gold standard of ranking relevance. In this paper, we decompose ranking relevance into content-based and engagement-based aspects, and we propose to leverage Large Language Models (LLMs) for both label and feature generation in model training, primarily aiming to improve the model's predictive capability for content-based relevance. Additionally, we introduce different sigmoid transformations on the LLM outputs to polarize relevance scores in labeling, enhancing the model's ability to balance content-based and engagement-based relevances and thus prioritize highly relevant items overall. Comprehensive online tests and offline evaluations are also conducted for the proposed design. Our work sheds light on advanced strategies for integrating LLMs into e-commerce product search ranking model training, offering a pathway to more effective and balanced models with improved ranking relevance.
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