AI 中文总结
针对拉丁美洲住房市场搜索难题及用户期望变化，提出基于大语言模型的重排器，依用户对话意图重排候选房源，构建评估数据集并验证，提升了搜索重排质量。
AI 中文摘要
QuintoAndar集团运营着拉丁美洲领先的租赁和销售住房市场平台，以全数字体验取代传统繁琐流程。在庞大房源目录中找理想住房很难，且对话式助手改变用户期望。为此提出基于大语言模型的重排器，依用户对话中细微、上下文丰富的意图对检索候选房源重新排序。构建大规模离线评估数据集，通过离线和在线A/B测试验证，结果显示重排质量有显著提升。
英文摘要
QuintoAndar Group operates the leading housing marketplace in Latin America for both rentals and sales. The platform replaces traditionally paper-heavy workflows with a fully digital experience, making housing transactions faster and more accessible to tenants, buyers, and landlords in the region. Finding the ideal home in such a vast catalog is inherently difficult. At the same time, the widespread adoption of conversational assistants is reshaping user expectations: people increasingly want to express their needs through open, multi-turn dialog rather than rigid filter menus and faceted search. This shift is particularly pronounced in housing, where intent is multi-dimensional, context-dependent, and rarely reducible to a small set of structured constraints. To meet these expectations, we propose a Large Language Model (LLM) based re-ranker that augments a conversational recommendation system by reordering retrieved candidates according to the nuanced, context-rich intent expressed across the user's conversation. We additionally construct a large-scale offline evaluation dataset for conversational real-estate search, containing 960,000 query-item pairs constructed from both synthetic and production queries and annotated using an LLM-as-a-Judge framework with human validation. We validate our approach both offline, on this proprietary dataset, and online, through a production A/B test. Both evaluations show consistent improvements in ranking quality, including a statistically significant increase in production of +5.3% in click-through rate and +4.8% in scheduled visits, demonstrating the value of integrating conversational context into housing recommendations.
