How Large Language Models Are Transforming E-Commerce Search

Abstract

E-commerce search has evolved from simple keyword matching to sophisticated semantic understanding through the adoption of large language models. Traditional search systems, reliant on lexical matching and inverted indexes, struggle with vocabulary mismatches, implicit user intent, and fragmented product information across text and images. Large language models fundamentally transform every layer of the search stack by introducing genuine language comprehension capabilities. These models employ transformer-based architectures with attention mechanisms to generate dense vector representations that capture semantic relationships between queries and products, enabling the retrieval of relevant items even without exact keyword overlap. They consolidate previously separate functions like category classification, attribute extraction, and query rewriting into unified systems that process natural language queries holistically. In conversational contexts, neural models maintain dialogue state and track user preferences across multiple turns, while multimodal architectures extract comprehensive product attributes by synthesizing information from text descriptions, packaging images, and product demonstrations. Vision-language models enable entirely new search paradigms where users can query with images alongside textual constraints, reasoning about visual style and semantic meaning simultaneously. By combining semantic search with structured metadata filtering, these systems expand discovery beyond literal matches to suggest complementary products, substitutes, and themed bundles based on usage contexts and culinary traditions encoded in pre-training data. This transformation represents a fundamental architectural shift from rigid keyword matching to flexible semantic understanding, creating search experiences that feel intuitive, reduce query reformulation, and surface relevant products that traditional systems systematically overlook.