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arXiv · 2601.16492

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

Emad Siddiqui·Venkatesh Terikuti·Xuan Lu
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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.

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Emad Siddiqui, Venkatesh Terikuti, Xuan Lu. 2026-01-23. LLM-based Semantic Search for Conversational Queries in E-commerce. https://arxiv.org/abs/2601.16492

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