Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition
This addresses the challenge of understanding complex user intent in product search for e-commerce platforms, though it is incremental by focusing on efficient deployment of LLM insights.
The paper tackled the problem of interpreting superlative expressions in e-commerce search queries by using LLMs to extract structured hints, resulting in a 10.9-point improvement in MAP and a 5.9-point improvement in MRR over baselines.
Search queries with superlatives (e.g., best, most popular) require comparing candidates across multiple dimensions, demanding linguistic understanding and domain knowledge. We show that LLMs can uncover latent intent behind these expressions in e-commerce queries through a framework that extracts structured interpretations or hints. Our approach decomposes queries into attribute-value hints generated concurrently with retrieval, enabling efficient integration into the ranking pipeline. Our method improves search performanc eby 10.9 points in MAP and ranking by 5.9 points in MRR over baselines. Since direct LLM-based reranking faces prohibitive latency, we develop an efficient approach transferring superlative interpretations to lightweight models. Our findings provide insights into how superlative semantics can be represented and transferred between models, advancing linguistic interpretation in retrieval systems while addressing practical deployment constraints.