## 5 Conclusions  
This work presents a significantly enhanced version of a student-housing recommender system, improving it from a local Python prototype into a scalable, intelligent system tailored to smart city environments. The platform introduces a modern client-server architecture using React and ExpressJS, enabling a more interactive, multi-user experience with real-time capabilities. Replacing static files with a cloud-hosted MongoDB database allows for efficient full-text and geospatial queries, forming the basis for responsive keyword filtering and spatial analytics. These architectural changes are not just technical upgrades but foundational shifts that improve system performance, scalability, and functionality. At the heart of the platform lies a hybrid recommendation engine that combines reinforcement learning and content-based filtering. This enables personalized, adaptive recommendations based on user preferences and interaction history, moving the tool from static filtering to dynamic learning. Live data ingestion is achieved through a Playwright-based scraper that continuously updates listings from the Storia platform. This transition from static to real-time data ensures that the platform reflects the current housing market, improving both recommendation relevance and user trust. The platform's integration of GeoJSON-based mapping and interactive visual analytics allows students to explore trends such as neighborhood-level rent heatmaps and spatial price distribution tools that make decision-making more informed and intuitive. For future work, several avenues remain open. One of these is the integration of more data sources, such as other listing websites or official housing databases, to enrich the platform's comprehensiveness [11]. Moreover, incorporating user accounts and collaborative filtering could allow the system to learn from the broader community of users (e.g., trends in what similar students chose) to complement the reinforcement-learning approach [12]. On the geospatial front, adding layers like public transport lines or real-time transit data could give users an idea of commute convenience for each listing. Another future direction is to refine the RL model using deeper neural network architectures (moving further into deep reinforcement learning) and possibly incorporating multi-step planning, which could consider a sequence of user interactions and preference changes. **Smart City and IoT Integration:** While our platform primarily deals with web data, it sets the stage for integration with broader smart city infrastructure. For example, further development could incorporate real-time data from urban sensors or IoT devices to augment housing decisions. In terms of IoT, smart apartments outfitted with sensors (for temperature, energy usage, and/or security) can broadcast metrics that could become part of a rental listing's profile in a smart city context (e.g., an energy-efficient home or the presence of smart security systems). Although these are beyond our current scope, the modular architecture is well-suited to add such data streams, aligning with the vision of a sensor-rich smart city environment where data-driven services help residents. **Implications for Smart Cities:** Our focus on student housing is one part of the smart city puzzle. However, the approaches used here could translate to other urban decision support scenarios. For example, a similar framework could help residents find **parking spots** (with IoT sensors providing real-time availability and RL recommending optimal choices based on schedule and walking distance) or help businesses identify **optimal store locations** using city demographic and foot-traffic data. The key is the fusion of real-time data, user-centric AI, and intuitive visualization--a pattern broadly applicable in smart cities. As municipalities continue to open up data and leverage AI, we expect to see more services that proactively assist citizens in everyday decisions like housing, using techniques akin to those we have demonstrated.