## 4 Discussion
_Search Efficiency:_ Number of listings viewed and filters applied before finding suitable options. _Recommendation Quality:_ Participants rated relevance on a 10-point Likert scale. _Trust Assessment_: Using the empirically validated Automation Trust Scale adapted for recommender systems [9]. _Results Analysis_: Despite the small sample size (\(n=10\)), the technical expertise of the participants enabled detailed feedback. Statistical analysis using [PERSON]'s test (appropriate for small samples) revealed significant differences across platforms (\(\chi^{2}\) (3) = 22.31, \(p<0.001\)). Post- hoc Wilcoxon signed-rank tests with Bonferroni correction confirmed our system's advantages [Tables 1 and 2]. The IT professionals particularly appreciated the technical features, with one participant noting: \"The explanation of why each listing was recommended made me trust the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Platform** & **Avg. Time (min)** & **Listings Viewed** & **Relevance Score** \\ \hline Our System & 8.3 \(\pm\) 2.1 & 5.2 \(\pm\) 1.8 & 8.7 \(\pm\) 0.9 \\ Imobiliare.ro & 24.6 \(\pm\) 6.3 & 18.4 \(\pm\) 5.2 & 6.2 \(\pm\) 1.3 \\ Facebook & 31.2 \(\pm\) 8.7 & 25.3 \(\pm\) 7.1 & 5.8 \(\pm\) 1.6 \\ Marketplace & 28.4 \(\pm\) 7.2 & 21.6 \(\pm\) 6.3 & 5.5 \(\pm\) 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Preprocessing** & **Query Time** & **Memory** & **Accuracy** & **Region Coverage** \\ \hline Fixed Boundaries & 0 ms & 2.3 \(\pm\) 0.4 ms & 0.5 MB & 82\% & 71\% \\ Dynamic & 45 \(\pm\) 8 ms & 4.7 \(\pm\) 0.8 ms & 3.2 MB & 94\% & 96\% \\ DBSCAN & 45 ms initial & 2.8 \(\pm\) 0.5 ms & 4.8 MB & 94\% & 96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational overhead comparison. system more than blind algorithmic feeds\". Non-IT participants valued the simplified interface, reporting a 40% lower cognitive load compared to traditional search interfaces. Qualitative analysis through structured interviews revealed that all 10 participants would recommend the system to incoming students. The mean System Usability Scale score of 84.5 (SD = 7.2) places our system in the \"excellent\" category according to Bangor's interpretation scale [10]. Participants specifically highlighted that seeing recommendation reasoning (proximity scores and price analysis) helped them make faster decisions, supporting findings from explainable AI research [11]. **System Response and Queries:** To validate the system's scalability, we conducted extensive load testing using Apache JMeter, simulating realistic scenarios such as the \"Peak Student Search Period\", characterized by intense user interactions with our API endpoints. Specifically, the tests simulated users progressively ramping up to high concurrency (500 simultaneous simulated users ramped over 60 s and maintained for 300 s), distributing requests among three critical endpoints: recommendations (/api/rentals/recommendations) at 40%, statistics (/api/rentals/stats/{rooms}) at 30%, and map visualizations (/api/rentals/map) at 30%. Performance results revealed exceptional responsiveness and stability across varying user loads:  
* 100 concurrent users: achieved a 98 th percentile latency of 145 ms with a 0% error rate. * 500 concurrent users: latency slightly increased to 312 ms at the 98 th percentile, with a minimal error rate of 0.1%. * 1000 concurrent users: even under extreme stress, the platform maintained a 98 th percentile latency of 890 ms and limited the error rate to just 0.8%. Geospatial query performance in MongoDB remained consistently fast, maintaining sub-100 ms response times even under a heavy load of 1000 concurrent requests. This efficiency is primarily due to optimized compound indexing on fields {mapped_zone: 1, price_amount: 1, scraped_at: -1}, significantly accelerating queries involving spatial filtering and pricing metrics.