Enhancing Co-Living Experience Through Intelligent Roommate Matching

Abstract

In today’s fast-paced urban environment, co-living has become a popular housing solution, particularly among students and young professionals seeking affordability, convenience, and community living. However, one of the major challenges in co-living is finding compatible roommates who share similar lifestyles and preferences. This paper proposes an intelligent roommate matching system integrated into a co-living application that uses data-driven algorithms to improve user satisfaction and community harmony. The study analyzes key factors influencing roommate compatibility, such as personality traits, habits, and lifestyle preferences. A prototype matching algorithm based on weighted similarity scoring is developed and evaluated through user feedback. The results demonstrate that intelligent roommate matching significantly enhances user experience, reduces conflicts, and promotes a balanced co-living environment.

Introduction

Co-living has emerged as a popular solution to urban housing challenges, offering affordable shared spaces and fostering community. However, many co-living setups suffer from roommate incompatibility due to inadequate matching methods that rely on basic criteria like gender or budget. To address this gap, the paper proposes an intelligent roommate-matching system that uses data-driven algorithms to improve compatibility, reduce conflicts, and enhance user satisfaction within co-living environments.

The literature shows that technology-driven housing platforms are reshaping shared living by incorporating personalization, trust, and user-centered design. Studies on roommate matching—particularly in dormitories and shared accommodations—demonstrate that personality alignment, lifestyle similarity, and behavioral compatibility significantly improve cohabitation outcomes. Advanced AI-based methods such as genetic algorithms, adaptive clustering, and hybrid recommender systems have shown strong potential but remain largely absent in commercial co-living applications. Existing platforms (Colive, CoHo, Zolo) provide only basic or filter-based matching, highlighting a clear opportunity for more sophisticated models that integrate social, psychological, and behavioral factors.

The proposed system addresses this gap by collecting detailed user data—demographics, lifestyle preferences, habits, personality traits, and interests—and storing it securely. Features are assigned weighted importance based on their influence on shared living harmony, with cleanliness and food preferences ranked highest. Using a weighted similarity algorithm implemented through SQL logic, the system computes compatibility scores between users, enabling accurate roommate recommendations. This approach enhances personalization, adaptability, and matching accuracy compared to traditional or rule-based systems.

Conclusion

The project successfully developed a database-driven and user-interactive roommate matching system that enhances the co-living experience through intelligent, data-supported recommendations. The system combines a structured relational database with a weighted SQL-based matching algorithm, enabling efficient and meaningful roommate pairing. The user interface, developed using react native, provides an interactive and intuitive experience that allows users to easily register, update their preferences, and view compatible roommate suggestions. Through a clean design and seamless navigation, users can explore their match results, communicate via the integrated chat feature, and update their information in real time. Any profile changes instantly trigger backend updates, ensuring that new compatibility scores are recalculated automatically. Testing with datasets validated that the system efficiently ranks users by compatibility, accurately reflecting similarities in lifestyle attributes such as food habits, cleanliness levels, and sleep schedules. The integration of a user-friendly front end with a robust backend database ensures both accessibility and performance, making the system a practical solution for modern co-living platforms. This work provides a strong foundation for intelligent roommate pairing by merging interactive design, structured data management, and algorithmic logic into one cohesive system that minimizes roommate conflicts and fosters a harmonious living environment. While the current system efficiently demonstrates database-based matching and an interactive interface, several enhancements can be incorporated to increase intelligence, scalability, and personalization: 1) Dynamic Machine learning Integration: Implement AI models to refine compatibility scores based on user feedback and previous match outcomes [17][18]. 2) Behavioural & Sentiment Analysis: Incorporate natural language processing (NLP) to analyse chat interactions and infer deeper compatibility metrics. 3) Enhanced Personalization: Use adaptive algorithms to automatically learn user preferences from in-app activity and adjust recommendations accordingly. 4) Improved UI/UX Design: Expand interface functionality with dashboards, dark mode, and personalized visualization of match statistics. 5) Chatbot Integration: Deploy an AI chatbot to assist with onboarding, conflict resolution, and profile setup. 6) Cloud Deployment & Scalability: Host the system on scalable cloud databases (e.g., Firebase or AWS) to accommodate large-scale usage. 7) Data Security and Verification: Employ blockchain or end-to-end encryption for secure profile verification and data integrity. By integrating these advancements, the system can evolve into a fully intelligent, adaptive roommate recommendation platform that not only suggests compatible roommates but also continuously learns and personalizes user experiences through a responsive, data-driven interface.

References

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Copyright

Copyright © 2025 Apoorva Bhalla, Preksha Sethi, Diti Ahuja, Aditya Raj Chugh. This 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.