标题： 位置的重要性：利用多分辨率地理嵌入进行住房搜索 显示英文标题

标题： Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search

摘要： QuintoAndar集团是拉丁美洲最大的住房平台，正在革新房产租赁和销售。 总部位于巴西，它通过消除文件工作并提高租户、买家和房东的可访问性来简化住房流程。 对于每个城市，都有数千套房屋可供选择，用户很难找到理想的家。 在这一背景下，位置起着关键作用，因为它显著影响房产价值、便利设施的可及性和生活质量。 一个好的位置可以使即便是普通住宅也极具吸引力。 因此，将位置纳入推荐中对于其有效性至关重要。 我们提出了一种地理感知嵌入框架，以解决数字租赁平台上住房推荐中的稀疏性和空间细微差别。 我们的方法在一个多层级的H3网格中整合到一个双塔神经架构中。 我们使用平台上的交互数据将我们的方法与传统的矩阵分解基线和单分辨率变体进行比较。 嵌入特定评估显示了更丰富且更平衡的嵌入表示，而离线排名模拟则显示出推荐质量的显著提升。 显示英文摘要

摘要： 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.