Customer behavioral data significantly impacts e-commerce search systems. However, in the case of less common queries, the associated behavioral data tends to be sparse and noisy, offering inadequate support to the search mechanism. To address this challenge, the concept of query reformulation has been introduced. It suggests that less common queries could utilize the behavior patterns of their popular counterparts with similar meanings. In Amazon product search, query reformulation has displayed its effectiveness in improving search relevance and bolstering overall revenue. Nonetheless, adapting this method for smaller or emerging businesses operating in regions with lower traffic and complex multilingual settings poses the challenge in terms of scalability and extensibility. This study focuses on overcoming this challenge by constructing a query reformulation solution capable of functioning effectively, even when faced with limited training data, in terms of quality and scale, along with relatively complex linguistic characteristics. In this paper we provide an overview of the solution implemented within Amazon product search infrastructure, which encompasses a range of elements, including refining the data mining process, redefining model training objectives, and reshaping training strategies. The effectiveness of the proposed solution is validated through online A/B testing on search ranking and Ads matching. Notably, employing the proposed solution in search ranking resulted in 0.14% and 0.29% increase in overall revenue in Japanese and Hindi cases, respectively, and a 0.08% incremental gain in the English case compared to the legacy implementation; while in search Ads matching led to a 0.36% increase in Ads revenue in the Japanese case.

翻译：客户行为数据对电子商务搜索系统具有显著影响。然而，对于较不常见的查询，其关联的行为数据往往稀疏且含有噪声，难以为搜索机制提供充分支持。为应对这一挑战，查询重构的概念被提出。该概念认为，较不常见的查询可以利用与其含义相似的流行查询的行为模式。在亚马逊产品搜索中，查询重构已展现出其在提升搜索相关性和增加整体收入方面的有效性。然而，将这种方法应用于流量较低、多语言环境复杂地区的小型或新兴企业时，在可扩展性和可扩展性方面面临挑战。本研究致力于克服这一挑战，构建一种即使在训练数据质量和规模有限、且语言特征相对复杂的情况下，仍能有效运行的查询重构解决方案。本文概述了在亚马逊产品搜索基础设施中实施的该解决方案，其涵盖了一系列要素，包括优化数据挖掘流程、重新定义模型训练目标以及重塑训练策略。所提解决方案的有效性通过在线A/B测试在搜索排序和广告匹配方面得到了验证。值得注意的是，在搜索排序中采用所提解决方案，与原有实现相比，在日语和印地语案例中分别实现了0.14%和0.29%的整体收入增长，在英语案例中实现了0.08%的增量收益；而在搜索广告匹配中，日语案例的广告收入增长了0.36%。