E-Commerce Search

E-commerce search connects shopper queries and preferences to product catalogs, aiming to improve discovery, relevance, and purchasing while serving marketplace goals. Systems combine lexical and vector retrieval, dense dual encoders, semantic product IDs and generative retrieval, then rerank results using behavioral, product-attribute, and multimodal signals. Language models also support query and intent understanding, conversational preference elicitation, and agentic shopping, with performance tested on relevance benchmarks and online measures such as clicks, conversion, and sales. A central challenge is balancing relevance and long-term user value with business objectives, while meeting precision, latency, cost, privacy, and safety constraints in production.