Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Silva, Ivo Nogueira, Pedro Bonaldo, Guilherme |
| author_facet | Silva, Ivo Nogueira, Pedro Bonaldo, Guilherme |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search Silva, Ivo Nogueira, Pedro Bonaldo, Guilherme Information Retrieval Machine Learning H.3.3; I.2.0 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. |
| title | Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search |
| topic | Information Retrieval Machine Learning H.3.3; I.2.0 |
| url | https://arxiv.org/abs/2510.01196 |