  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.
