Improving Item Discoverability in e-Commerce Search via Related Intent Generation

Discovery-augmented e-commerce search via LLM intent generation with teacher-student distillation

Researchers present a scalable system for expanding search recall in e-commerce (particularly grocery) by generating implicit user intents using LLMs, then distilling that capability into a fine-tuned small language model via LoRA and teacher-student distillation. The two-stage hybrid architecture extends discovery coverage from ~60% to ~80% of query traffic at roughly 30% of the teacher model's inference cost. Evaluation combines LLM-as-a-judge quality metrics validated against human preferences with end-to-end session-level purchase analysis.