LLM-based Semantic Search for Conversational Queries in E-commerce

2026-01-23

AI Summary

This work addresses the challenge of accurately interpreting complex user intents expressed in natural language within traditional e-commerce search systems. We propose a semantic search framework that integrates large language models (LLMs) to jointly optimize an embedding model and a structured constraint generation module, enabling precise intent extraction from conversational queries and facilitating synergistic semantic retrieval with attribute-based filtering. To mitigate the scarcity of labeled data, we leverage LLMs to generate high-quality synthetic data for embedding fine-tuning and devise an end-to-end training strategy. Experimental results on real-world e-commerce datasets demonstrate that our approach significantly outperforms existing baselines, achieving notable improvements in both retrieval precision and recall.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Conversational user queries are increasingly challenging traditional e-commerce platforms, whose search systems are typically optimized for keyword-based queries. We present an LLM-based semantic search framework that effectively captures user intent from conversational queries by combining domain-specific embeddings with structured filters. To address the challenge of limited labeled data, we generate synthetic data using LLMs to guide the fine-tuning of two models: an embedding model that positions semantically similar products close together in the representation space, and a generative model for converting natural language queries into structured constraints. By combining similarity-based retrieval with constraint-based filtering, our framework achieves strong precision and recall across various settings compared to baseline approaches on a real-world dataset.

Problem

• conversational queries
• semantic search
• e-commerce
• user intent
• keyword-based search

Innovation

• LLM-based semantic search
• conversational queries
• synthetic data generation
• embedding model
• structured constraint generation