## 2 Research Questions and Methodology
### Methodology  
To address the research questions, this study employed a multifaceted approach that included a literature review, prototype development, and practical evaluation. The literature review focused on existing research in open data visualization, real-estate data processing, and other rental applications, offering insights into current trends and challenges. While traditional recommender system approaches have been applied to this domain, acknowledging the complexity of housing decisions influenced by factors such as location, amenities, and price, \"A User-Centric Housing Recommender System\" [4] introduced a hybrid housing recommendation model that combined fuzzy logic with item-based collaborative filtering. This approach utilized fuzzy rules to interpret imprecise user preferences and filtered listings accordingly [28]. However, their system did not incorporate reinforcement learning or interactive map features, relying instead on static preference modeling [29]. Another notable advancement is the integration of augmented reality (AR) with basic recommender algorithms in rental platforms [5]; the \"Smart Rent Portal using Recommendation System Visualized by Augmented Reality\" was developed, which enabled users to search for rental properties using a preference-based collaborative filtering recommender and explore apartment interiors through AR visualizations. Their system featured a Node.js (runtime)/20.11.1 LTS and Express backend, utilized a MongoDB database for storing property listings, and demonstrated the practicality of a web-based rental search application. In contrast, our work introduces a reinforcement-learning framework to continually improve recommendations based on user interactions, which has shown promise in other recommendation domains for capturing dynamic preferences [9]. The article selection process was guided by the PRISMA framework, as illustrated in Figure 1. This diagram outlines the systematic approach used to identify, screen, and include relevant studies for review. Keywords: \"data visualization\", \"real estate data processing\" Timeframe: 2010-2025, Sort by relevancy. During manuscript preparation, graphical icons were prototyped with the assistance of **ChatGPT 04-mini (OpenAI, San Francisco, CA, USA)** under direct human supervision. All AI-generated material was subsequently reviewed and edited by the authors, who take full responsibility for the final content. The second part of the methodology involved developing a web application prototype with a React frontend and an ExpressJS + MongoDB backend, deployed serverless on Vercel. This platform is designed to address real-world challenges faced by students seeking rental housing near universities in Timisoara [6]. It leverages open data sources and zone-based geospatial enrichment (e.g., computing each listing's proximity to campus zones) to provide real-time, context-rich rental information. Interactive mapping visualizations are combined with a reinforcement-learning model that ranks and recommends listings based on user interactions, improving search efficiency beyond traditional filter-based methods (RQ1). Furthermore, the integration of open data and a free, serverless deployment ensures the platform remains accessible and up-to-date for student users, illustrating the benefits of using open, real-time geospatial data and cloud deployment in a student-focused housing platform (RQ2). The development itself spans multiple layers of the platform, from data acquisition to AI model design and user interface development. In this section, we describe how each major component of the system was designed and implemented, and how they work together to deliver an intelligent rental recommendation service [6].