UMate : Bumble for Roommates

Ever had difficulty finding a roommate? Just click on UMate and find your perfect match at UMass!

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Inspiration

Ever had difficulty finding a roommate? Tired of endless roommate search across WhatsApp, Telegram, Facebook? Just click and find your perfect UMass roommate match!

UMate is a web application designed to simplify the process of finding compatible roommates. With UMate, users can easily create a profile, set their preferences, and find potential roommates using a swipe interface similar to dating apps. By using a recommendation model, UMate suggests the top matches based on compatibility factors like the duration of stay, lifestyle, budget, dietary preferences, and more.

What it does?

UMate is a roommate-finding web application designed to help users find compatible roommates through a swipe-based interface similar to dating apps. It uses a recommendation model to suggest top matches based on compatibility factors like lifestyle, budget, and dietary preferences.

Key Features:

  • User Authentication: Secure login and sign-up.
  • User Profiles: Customizable profiles with details such as budget, dietary preferences, and smoking habits.
  • Recommendation System: Recommends the top 5 matches based on various compatibility factors.
  • Filtering Options: Filter potential roommates by preferences like budget, lifestyle, dietary preferences.

How we built it?

Tech Stack:

  • Frontend: React
  • Backend: Flask (Python)
  • Database: MongoDB
  • Cloud Services: Amazon Web Services (AWS) - S3 for file storage

Challenges we ran into:

  • CORS Issues: We encountered Cross-Origin Resource Sharing (CORS) problems while integrating the frontend with the backend. Ensuring secure communication between the React frontend and Flask backend required configuring CORS policies correctly, especially for handling API requests smoothly.

  • Flask Integration for the Recommendation Model: Integrating the recommendation model into the Flask backend was challenging. This involved setting up efficient API endpoints that could handle and process recommendation logic, ensuring real-time data handling and accurate matching.

  • Data Processing for Recommendations: Preparing the data for recommendation was complex, requiring thorough preprocessing of user profiles and preferences. It was essential to clean, standardize, and transform data to accurately align with the compatibility factors used by the recommendation model.

Accomplishments that we're proud of

  • Addressing a Real Problem: UMate directly solves a common problem UMass students face in finding compatible roommates. By focusing on this need, we developed a platform that makes the roommate search process faster, simpler, and more accurate.

  • User Authentication & Profile Customization: We successfully implemented secure login and profile features, allowing users to specify preferences like budget, dietary needs, and lifestyle. This customization is crucial for creating detailed profiles, ensuring that users are matched with compatible roommates.

  • Powerful Recommendation System: Our recommendation model accurately suggests the top 5 matches for each user, using factors such as budget, lifestyle, and stay duration. This feature helps students at UMass find roommates who best align with their lifestyle preferences and needs.

  • Filtering Options for Precision: We developed a filtering system that lets users refine their roommate search according to personal criteria like dietary restrictions and budget. This customization empowers users to find their ideal match, reducing the complexity of their search.

  • User-Friendly Swipe Interface: Our swipe-based interface, inspired by dating apps, allows users to browse matches quickly and intuitively, enhancing user engagement and making the process enjoyable.

What we learned?

  • Handling CORS and API Integrations: We resolved CORS issues to enable smooth communication between the React frontend and Flask backend, deepening our understanding of secure API integrations and cross-origin policies.

  • Recommendation Model Development: We learned the intricacies of creating a recommendation model that balances various user preferences, ensuring that our suggestions are accurate and beneficial for users.

  • Data Preprocessing for Improved Accuracy: We gained insights into the importance of data preprocessing, including cleaning and normalizing data, to enhance recommendation accuracy and model performance.

  • Extensive React.js Implementation: Building the UI with React.js provided valuable experience in designing a responsive and dynamic user interface, helping us create an engaging experience for users.

  • Solving Real-World Problems: By addressing a common issue for UMass students, we learned how to design a solution that meets real needs, reinforcing the importance of user-centered design in making impactful tech solutions.

What's next for UMate : Bumble for Roommates

  • Make it live on AppStore

  • Enhanced Matching Algorithm: We plan to refine the recommendation model by incorporating more personalized factors, such as shared hobbies or specific living preferences, to improve match accuracy and user satisfaction.

  • Social Media Integration: Just like Bumble, integrating social media handles into the user profiles could allow users to verify their personalities and lifestyles, providing a more holistic view of potential roommates.

  • Real-Time Chat Feature: Adding a real-time chat feature would enable users to communicate with potential roommates directly within the app, streamlining the process of finalizing a match and moving forward with plans.

  • Roommate Reviews and Ratings: To build more trust in the roommate search process, we could implement a review and rating system where users can leave feedback on their previous roommates, helping future users make informed decisions.

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