Intent-Aware Neural Query Reformulation for Behavior-Aligned Product Search

TL;DR

This paper presents a novel intent-aware query reformulation framework for e-commerce search, leveraging large-scale query logs and advanced modeling to improve relevance and user engagement.

Contribution

It introduces a data pipeline for mining intent signals and develops adaptive query rewriting strategies grounded in user intent, advancing search relevance in e-commerce.

Findings

Measurable improvements in relevance metrics across product categories

Effective extraction of fine-grained intent signals from query logs

Scalable framework for intent-aware query reformulation

Abstract

Understanding and modeling buyer intent is a foundational challenge in optimizing search query reformulation within the dynamic landscape of e-commerce search systems. This work introduces a robust data pipeline designed to mine and analyze large-scale buyer query logs, with a focus on extracting fine-grained intent signals from both explicit interactions and implicit behavioral cues. Leveraging advanced sequence mining techniques and supervised learning models, the pipeline systematically captures patterns indicative of latent purchase intent, enabling the construction of a high-fidelity, intent-rich dataset. The proposed framework facilitates the development of adaptive query rewrite strategies by grounding reformulations in inferred user intent rather than surface-level lexical signals. This alignment between query rewriting and underlying user objectives enhances both retrieval relevance and downstream engagement metrics. Empirical evaluations across multiple product verticals demonstrate measurable gains in precision-oriented relevance metrics, underscoring the efficacy of intent-aware reformulation. Our findings highlight the value of intent-centric modeling in bridging the gap between sparse user inputs and complex product discovery goals, and establish a scalable foundation for future research in user-aligned neural retrieval and ranking systems.