Search query variation poses a challenge in e-commerce search, as equivalent
search intents can be expressed through different queries with surface-level
differences. This paper introduces a framework to recognize and leverage query
equivalence to enhance searcher and business outcomes. The proposed approach
addresses three key problems: mapping queries to vector representations of
search intent, identifying nearest neighbor queries expressing equivalent or
similar intent, and optimizing for user or business objectives. The framework
utilizes both surface similarity and behavioral similarity to determine query
equivalence. Surface similarity involves canonicalizing queries based on word
inflection, word order, compounding, and noise words. Behavioral similarity
leverages historical search behavior to generate vector representations of
query intent. An offline process is used to train a sentence similarity model,
while an online nearest neighbor approach supports processing of unseen
queries. Experimental evaluations demonstrate the effectiveness of the proposed
approach, outperforming popular sentence transformer models and achieving a
Pearson correlation of 0.85 for query similarity. The results highlight the
potential of leveraging historical behavior data and training models to
recognize and utilize query equivalence in e-commerce search, leading to
improved user experiences and business outcomes. Further advancements and
benchmark datasets are encouraged to facilitate the development of solutions
for this critical problem in the e-commerce domain.

在电子商务搜索中，搜索查询的变化构成了挑战，因为等效的搜索意图可以通过不同的查询表达出来。本文介绍了一个框架，以识别和利用查询等效性来增强搜索者和商业结果。该方法解决了三个核心问题：将查询映射到搜索意图的向量表示、识别表示等效或相似意图的最近邻查询、以及针对用户或业务目标进行优化。该框架利用表面相似性和行为相似性来确定查询等效性。表面相似性涉及基于单词变化、单词顺序、合成以及噪声词对查询进行规范化。行为相似性利用历史搜索行为生成查询意图的向量表示。离线过程用于训练句子相似性模型，而在线的最近邻方法支持处理未见过的查询。实验评估表明了该方法的有效性，优于流行的句子转换模型，并实现了 0.85 的查询相似性皮尔逊相关系数。结果突显了利用历史行为数据和训练模型来识别和利用电子商务搜索中的查询等效性的潜力，提升用户体验和业务结果。鼓励进一步的进展和基准数据集，以促进解决电子商务领域中这一关键问题的解决方案的发展。