  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.
