In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue. Along this line, query rewriting, serving as an important technique to bridge the semantic gaps inherent in the semantic matching process, has attached wide attention from the industry and academia. However, existing query rewriting methods often struggle to effectively optimize long-tail queries and alleviate the phenomenon of "few-recall" caused by semantic gap. In this paper, we present BEQUE, a comprehensive framework that Bridges the sEmantic gap for long-tail QUEries. In detail, BEQUE comprises three stages: multi-instruction supervised fine tuning (SFT), offline feedback, and objective alignment. We first construct a rewriting dataset based on rejection sampling and auxiliary tasks mixing to fine-tune our large language model (LLM) in a supervised fashion. Subsequently, with the well-trained LLM, we employ beam search to generate multiple candidate rewrites, and feed them into Taobao offline system to obtain the partial order. Leveraging the partial order of rewrites, we introduce a contrastive learning method to highlight the distinctions between rewrites, and align the model with the Taobao online objectives. Offline experiments prove the effectiveness of our method in bridging semantic gap. Online A/B tests reveal that our method can significantly boost gross merchandise volume (GMV), number of transaction (#Trans) and unique visitor (UV) for long-tail queries. BEQUE has been deployed on Taobao, one of most popular online shopping platforms in China, since October 2023.

翻译：在电子商务搜索领域，语义匹配的重要性不容忽视，因为它直接影响用户体验和公司收入。沿着这一方向，查询重写作为弥合语义匹配过程中固有语义差距的重要技术，受到了工业界和学术界的广泛关注。然而，现有的查询重写方法往往难以有效优化长尾查询，并缓解由语义差距导致的“少召回”现象。在本文中，我们提出了BEQUE，一个弥合长尾查询语义差距的综合框架。具体而言，BEQUE包含三个阶段：多指令监督微调（SFT）、离线反馈和目标对齐。我们首先基于拒绝采样和辅助任务混合构建重写数据集，以监督方式微调我们的大语言模型（LLM）。随后，利用训练好的LLM，我们采用束搜索生成多个候选重写，并将其输入淘宝离线系统以获取偏序。利用重写的偏序，我们引入对比学习方法以突出重写之间的差异，并使模型与淘宝在线目标对齐。离线实验证明了我们方法在弥合语义差距方面的有效性。在线A/B测试显示，我们的方法能显著提升长尾查询的总商品交易额（GMV）、交易笔数（#Trans）和独立访客数（UV）。自2023年10月起，BEQU已部署于中国最受欢迎的在线购物平台之一——淘宝。