Leveraging Large Language Models for Search Relevance Measurement in E-Commerce

Abstract

Search relevance measurement represents a critical challenge in e-commerce systems, where the accuracy of query-item matching directly impacts user experience, conversion rates, and platform trust. Traditional approaches to measuring relevance have relied heavily on human annotation and behavioral signals, both of which present significant limitations in scalability, cost, and accuracy. Human evaluation suffers from inter-rater variability, limited coverage of long-tail queries, and prohibitive resource requirements when applied to large product catalogs. Implicit feedback signals such as clicks and conversions introduce noise through position bias and popularity effects, often failing to reflect true relevance. Recent advances in large language models offer a transformative alternative by leveraging semantic understanding, contextual reasoning, and world knowledge to assess query-item relationships at scale. Through careful prompt engineering, chain-of-thought reasoning, and validation against human-labeled datasets, these models can generate reliable relevance judgments that approximate or exceed human performance while covering vastly larger evaluation spaces. Implementation strategies, including teacher-student architectures, active learning for edge cases, and periodic human audits, enable organizations to balance accuracy with operational efficiency. This article addresses fundamental trade-offs between coverage, quality, and cost that have long constrained traditional relevance measurement methodologies, enabling more sophisticated search systems that genuinely understand and respond to user intent rather than merely optimizing for engagement metrics.