AI Guided Accelerator For Search Experience

2025-07-25

arXiv.org

AI Summary

Traditional query rewriting methods typically model isolated query pairs, failing to capture the inherent intent evolution and sequential dynamics characteristic of exploratory search—leading to misalignment between retrieved products and users’ actual needs in e-commerce. This work formally defines “transitional queries” and proposes a structured sequence mining approach grounded in user behavioral logs to model intent flows and construct intent-consistent query evolution chains. Furthermore, it integrates large language models (LLMs) to enable semantically rich, intent-controllable generative query expansion. Deployed on a real-world e-commerce platform, the method yields significant improvements in conversion rate and user engagement. Empirical evaluation on related search tasks demonstrates measurable performance gains over established baselines, validating both the modeling framework and the LLM-augmented generation strategy.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search
• Natural Language Processing: Question Answering

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 incorporate generative Large Language Models (LLMs) to produce semantically diverse and intent-preserving alternative queries, extending beyond what can be derived through collaborative filtering alone. These reformulations can be leveraged to populate Related Searches or to power intent-clustered carousels on the search results page, enhancing both discovery and engagement. Our contributions include (i) the formal identification and modeling of transitional queries, (ii) the introduction of a structured query sequence mining pipeline for intent flow understanding, and (iii) the application of LLMs for scalable, intent-aware query expansion. Empirical evaluation demonstrates measurable gains in conversion and engagement metrics compared to the existing Related Searches module, validating the effectiveness of our approach in real-world e-commerce settings.

Problem

• Model transitional queries in e-commerce search behavior
• Mine structured query sequences to understand intent shifts
• Apply LLMs for intent-aware query expansion and discovery

Innovation

• Models transitional queries from user journey sequences
• Mines structured query trajectories from interaction logs
• Uses LLMs for intent-aware query expansion