AI Guided Accelerator For Search Experience
Jayanth Yetukuri, Mehran Elyasi, Samarth Agrawal, Aritra Mandal, Rui Kong, Harish Vempati, Ishita Khan

TL;DR
This paper introduces a novel framework for modeling transitional queries in e-commerce search, utilizing query sequence mining and large language models to improve search relevance and user engagement.
Contribution
It formalizes the concept of transitional queries, develops a structured query sequence mining pipeline, and applies LLMs for scalable, intent-aware query expansion.
Findings
Measurable improvements in conversion rates.
Enhanced user engagement metrics.
Effective modeling of user intent transitions.
Abstract
Effective query reformulation is pivotal in narrowing the gap between a user's exploratory search behavior and the identification of relevant products in e-commerce environments. While traditional approaches predominantly model query rewrites as isolated pairs, they often fail to capture the sequential and transitional dynamics inherent in real-world user behavior. In this work, we propose a novel framework that explicitly models transitional queries--intermediate reformulations occurring during the user's journey toward their final purchase intent. By mining structured query trajectories from eBay's large-scale user interaction logs, we reconstruct query sequences that reflect shifts in intent while preserving semantic coherence. This approach allows us to model a user's shopping funnel, where mid-journey transitions reflect exploratory behavior and intent refinement. Furthermore, we…
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Taxonomy
TopicsRobotics and Automated Systems · Artificial Intelligence in Games
