A Smart Housing Recommender for Students in Timișoara: Reinforcement Learning and Geospatial Analytics in a Modern Application
Rental accommodations near European university campuses keep rising in price, while listings remain scattered and opaque. This paper proposes a solution that overcomes these issues by integrating real-time open listing ingestion, zone-level geospatial enrichment, and a reinforcement-learning recomme...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7869 |
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| Summary: | Rental accommodations near European university campuses keep rising in price, while listings remain scattered and opaque. This paper proposes a solution that overcomes these issues by integrating real-time open listing ingestion, zone-level geospatial enrichment, and a reinforcement-learning recommender into one streamlined analysis pipeline. On demand, the system updates price statistics for most districts in Timișoara and returns five budget-safe offers in a short amount of time. By combining adaptive ranking with new spatial metrics, it significantly cuts search time and removes irrelevant offers in pilot trials. Moreover, this implementation is fully open-data, open-source, and free, designed specifically for students to ensure accessibility, transparency, and cost efficiency. |
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| ISSN: | 2076-3417 |