Semantic Equivalence of e-Commerce Queries

TL;DR

This paper presents a framework for recognizing and leveraging query equivalence in e-commerce search by combining surface and behavioral similarity, improving search accuracy and business outcomes.

Contribution

It introduces a novel approach that integrates surface and behavioral query similarities, with an offline training process and online retrieval, outperforming existing models.

Findings

Achieved a Pearson correlation of 0.85 in query similarity evaluation.

Outperformed popular sentence transformer models in experiments.

Demonstrated improved search relevance and user experience.

Abstract

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.