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

• URL: http://arxiv.org/abs/2601.16492v1
• Date: Fri, 23 Jan 2026 06:35:28 GMT
• Title: LLM-based Semantic Search for Conversational Queries in E-commerce
• Authors: Emad Siddiqui, Venkatesh Terikuti, Xuan Lu,
• Abstract summary: We present an LLM-based semantic search framework that captures user intent from conversational queries.<n>Our framework achieves strong precision and recall across various settings compared to baseline approaches on a real-world dataset.
• Score: 1.3645712130536118
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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