By generating implicit user intents to expand search results, e-commerce platforms can surface complementary and substitute products while maintaining relevance, improving both user satisfaction and visibility for long-tail inventory.
This paper presents a system that improves product discovery in e-commerce by generating related search intents beyond exact query matches. Using large language models for popular queries and fine-tuned smaller models for niche queries, the system expands what products users see while keeping results relevant—increasing discovery coverage from 60% to 80% at lower computational cost.