  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.
