Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{169266,
        author = {Tamish Gambhir and Sneha Singh and Sheenam Naaz},
        title = {Room Recommendation System For Patients Near Hospitals using Collaborative Filtering, Content Filtering, and Feature Engineering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {580-592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169266},
        abstract = {In today's dynamic society, finding a compatible room is a crucial step towards fostering a harmonious living environment. Traditional methods of room selection often rely on subjective judgments and limited information, leading to suboptimal matches and potential conflicts. To address this challenge, we propose a room recommendation system designed to streamline the room selection process and enhance the overall living experience.
It leverages advanced data analytics techniques and machine learning algorithms to match individuals based on their preferences, habits, and personalities. The system collects comprehensive user data through intuitive interfaces, including demographic information, lifestyle preferences, and desired living arrangements. Through sophisticated feature engineering and collaborative filtering algorithms, it generates personalized room recommendation near hospitals tailored to each user's unique profile. Furthermore, our model prioritizes user privacy and fairness by implementing robust data protection measures and ensuring algorithmic transparency. 
In conclusion, this idea represents a significant advancement in room recommendation systems, offering a data-driven approach to room matching that enhances living experiences and fosters positive social interactions. As society continues to evolve, this stands poised to revolutionize the way individuals find compatible rooms, ultimately contributing to happier and more fulfilling living arrangements.},
        keywords = {Recommendation systems, Personalized room recommendation system, Machine Learning, Collaborative filtering, Content filtering, Feature Engineering},
        month = {November},
        }