用多分辨率地理嵌入提升租房推荐精准度
Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search
- 构建分层H3网格嵌入，融合多尺度地理信息
- 在真实平台数据上，推荐质量显著优于基线方法
- 适合做房产推荐系统、空间智能产品的研发人员
QuintoAndar集团是拉丁美洲最大的住房平台，总部位于巴西，通过简化流程、消除文书工作，提升了租客、买家和房东的体验。平台上每个城市都有数千套房源，用户难以找到理想住所。在此背景下，地理位置对房价、生活品质和配套设施可达性具有关键影响，优质位置可使普通房源变得极具吸引力。因此，将位置信息融入推荐至关重要。本文提出一种地理感知嵌入框架，解决数字租赁平台中位置信息稀疏与空间细微差异问题。该方法将分层H3网格在多个层级上整合进双塔神经网络架构。我们对比了该方法与传统矩阵分解基线及单分辨率变体，在平台真实交互数据上的实验表明，嵌入表示更丰富均衡，离线排名模拟显示推荐质量显著提升。
原文摘要 · Abstract (English)
QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendation quality.
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