用大模型理解对话上下文，提升房产搜索推荐精准度。
LLM-Based Re-Ranking for Real Estate Search
- 基于大模型分析用户多轮对话，动态重排搜索结果。
- 线上测试点击率提升5.3%，预约看房量增4.8%。
- 适用于需要自然语言交互的个性化推荐场景。
QuintoAndar集团运营拉丁美洲领先的房屋交易数字平台，覆盖租赁与买卖。平台将传统纸质流程数字化，使租房、购房和出租更高效便捷。面对海量房源，用户寻房难度高。同时，对话助手的普及改变了用户期望：人们更愿通过开放式的多轮对话表达需求，而非固定筛选菜单。这一趋势在房产领域尤为明显，因需求具有多维性、上下文依赖性，难以用少量结构化条件概括。为此，我们提出一种基于大语言模型（LLM）的重排序机制，通过分析用户对话中的细粒度意图，对召回候选房源进行动态重排。我们还构建了一个大规模离线评估数据集，包含96万条查询-房源对，来自合成及生产环境查询，并采用大模型作为裁判（LLM-as-a-Judge）框架结合人工验证进行标注。我们在该数据集上进行离线验证，并在生产环境中开展A/B测试。结果一致显示排名质量提升，线上点击率提高5.3%，预约看房量增加4.8%，证明融入对话上下文能显著增强房产推荐效果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。