LLM-Based Re-Ranking Improves Real Estate Search Outcomes
Researchers at QuintoAndar Group developed a large language model (LLM)-based re-ranking system for conversational real estate search. Using an LLM-as-a-Judge framework and a dataset of 960,000 query-item pairs with human validation, they demonstrated that their approach led to a 5.3% increase in click-through rate and a 4.8% increase in scheduled visits in production A/B tests.
Why it matters: This work shows a significant, real-world improvement in search effectiveness for a major real estate platform using LLMs, with measurable business impact.
Full story at: arXiv Information Retrieval ↗