## 4 Discussion
To further enhance scalability and efficiency in reinforcement-learning (RL) inference, we developed a highly optimized strategy involving batch processing, caching, and parallel execution. Requests are grouped into batches of 32, significantly reducing redundant computations. Each inference request first checks an LRU cache (capped at 10,000 entries with a 5 min TTL) to quickly return cached predictions. For uncached requests, the system leverages parallel execution with up to four workers, thereby ensuring rapid inference. These combined strategies resulted in an impressive 41.66% reduction in mean absolute error and a dramatic 96.63% decrease in computational cost, attributed to our specialized Fast Spatial-Temporal Information Compression algorithm [13]. These optimizations enable the platform to robustly handle extreme traffic spikes, such as those observed during university admission periods, when user loads can surge to 10\(\times\) normal levels, maintaining responsive and seamless user experiences. Using MongoDB for search and aggregation proved efficient. Keyword searches typically took under 200 ms on the dataset, and geospatial queries (e.g., \"within 2 km of campus\") under 100 ms, as measured on the Atlas cluster with appropriate indexes. Rendering the map was smooth in the browser, aided by Mapbox's WebGL optimizations 2.16.1 (Mapbox Inc., Washington, DC, USA). The aggregation for neighborhood statistics (average price per zone) was computed in under 50 ms on the database side, and the results were cached for the session, making the heatmap toggle instantaneous for the user. The entire round-trip for a recommendation refresh (from user clicking refresh to new recommendations and updated visuals showing) was around 1-2 s, which is acceptable for an interactive web application. This is a notable improvement from the Streamlit prototype, which often took several seconds to recompute recommendations or refresh visualizations due to its single-threaded, server-side nature. **Visualization Insights:** The geospatial and chart-based visualizations provided tangible insights in our case study. The neighborhood heatmap clearly showed the gradient of rents: central areas around the main university campus had higher median rents, whereas peripheral residential areas more faded, indicating lower median rents. This aligned with known trends, but seeing it on the map allowed students to quickly target the affordable zones. The scatter plot of rent identifying these outliers can be valuable; a student open to shared housing might spot a cheap central listing that they would otherwise overlook among more expensive ones. **System Integration Challenges:** Building an end-to-end system that combines web development with AI and data engineering posed challenges. One challenge was ensuring the RL model's recommendations remained transparent and explainable to users. Students might want to know why a certain listing is recommended (e.g., is it because it is very close to campus, or a good price, or matches a pattern of what they liked?). To address this, we included some explainability features: the UI can highlight a few key reasons (like tags \"Close to campus\" or \"Below budget\") on the recommended listings, derived from the features that influenced the ranking. This kind of transparency is important in gaining user trust in AI recommendations. The results overall demonstrate that the integration of reinforcement learning and geospatial analytics within a modern web framework can significantly enhance a student rental search platform. Students can find suitable housing more efficiently by receiving intelligent recommendations and by exploring data-driven visuals that make the rental market more transparent.