## 4 Discussion  
The results cover the performance of the data pipeline, the effectiveness of the recommendation engine, and the usability of the visualization features. **Data Collection and Coverage:** To ensure reliable and efficient data collection, we used the publicly accessible Storia API (OLX Group B.V., Amsterdam, The Netherlands) as our primary source for rental listings. The API allows programmatic querying of listings via HTTPS GET requests with customizable parameters such as _offer_type=rent_, _estate_type=apartment_, _location=timisora_, and pagination controls like _pagination[offset]_ and _pagination[limit]_. For example, a typical request query would follow this structure:  
_[[https://www.storia.ro/api/offers?offer_type=rentestate_type=apart-_](https://www.storia.ro/api/offers?offer_type=rentestate_type=apart-_)]([https://www.storia.ro/api/offers?offer_type=rentestate_type=apart-_](https://www.storia.ro/api/offers?offer_type=rentestate_type=apart-_))  
_ment&city=timisora&_pagination[offset]=0&_pagination[limit]=50_  
This returns a JSON response containing metadata and content fields such as price, rooms, location, description, features, and coordinates, which are parsed into and stored directly in our MongoDB collection. The use of the API ensures structured, complete, and timely data extraction without the overhead of rendering dynamic frontend content. To maintain data freshness, our system periodically paginates through the available listings and compares last_refresh_date fields to detect new or updated entries. The Playwright-based scaper was able to gather a substantial dataset of rental listings. For a case study in Timisoara (a university city), the scaper collected approximately 1200 active rental listings over a two-month period. These listings spanned 20 distinct neighborhoods/zones of the city, with rent prices ranging from approximately EUR 150 to EUR 600 per month and properties from studios to three-bedroom apartments. The continuous crawling ensured that in any given week, the database reflected the current market; on average, about 50 new listings were added weekly, and a smaller number were removed as they were rented out. This up-to-date dataset forms a solid foundation for generating relevant recommendations [5]. For the performance and user-study runs, the engine processed the current active set of 1238 listings (\(\approx\)2.6% of the full 47,583-listing history), mirroring what a student actually sees at search time. **Recommendation Engine Performance:** To rigorously evaluate system effectiveness, we conducted a comprehensive comparative study [6]. The study involved our 10 volunteers, graduates from master's programs, with a mean age of 26 years. All participants are also employees, with about 70% working in the IT area, providing valuable technical insights into system usability. _Evaluation Framework:_ Given the specific profile of our participants, we designed a within-subjects study where each volunteer completed identical housing search tasks on four different platforms over a two-month evaluation period. _Platforms Evaluated:_ Our system: reinforcement learning with explainable recommendations. Imobiliare.ro: Romania's largest property portal using traditional faceted search. Facebook Marketplace (Meta Platforms Inc., Menlo Park, CA, USA): a social commerce platform, increasingly popular among young professionals. OIX Romania: a classified ads platform with basic search functionality [40]. The evaluation protocol required each participant to search for rental accommodation suitable for a hypothetical new master's student at their alma mater. This approach leveraged their recent experience while standardizing the search task. The platform order was randomized using a Latin square design to eliminate learning effects [7]. _Measurement Instruments_: We employed both quantitative metrics and qualitative feedback as follows:  
_Time to Satisfactory Result_: Measured from search initiation to identifying a listing they would contact.