RooME

Roommate Matching App

Welcome to the RooME, an innovative app aimed at helping people find their perfect roommates in a modern, Tinder-like experience. For renters and seekers looking to find compatible living arrangements.

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Link to Full Design Document

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Table of Contents

1. Overview
2. Key Features
3. System Design
   • Diagrams
   • App Routines
   • Datasets
4. Roommate Matching Logic
5. Next Steps

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Overview

The RoOME app is designed to cater to individuals looking for a more modern, user-friendly, and personalized roommate search tool. The core concept of the app is to match users based on personality traits, preferences, lifestyle choices, and housing needs.

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Key Features

• Profile Matching: Matches individuals based on shared interests, lifestyles, budgets, and location preferences.
• Swipe-to-Match: Similar to Tinder, users swipe left to reject or right to accept potential roommates.
• In-App Messaging: Users can chat with potential roommates before deciding to meet in person.
• Location-Based Searching: Matches users based on geographic proximity.
• Room Listings & Search: Users can list available rooms and apartments and search for available places based on their preferences.

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System Design

Diagrams

Here’s an overview of the system design for Dating Disguised As Roomies Finder:

1. Roommate Matching System Diagram

This diagram shows the key components involved in matching potential roommates based on user profiles, preferences, and location. '''sql +-------------------+ +-------------------+ | User Profile | <---> | Preferences/ | | Data (Location, | | Compatibility | | Budget, Likes) | | Score Calculators| +-------------------+ +-------------------+ |
v
+-------------------+
| Matchmaking |
| Algorithm | +-------------------+
| v +-------------------+ | Roommate Matches | +-------------------+

'''

2. User Journey Flow Diagram

This diagram illustrates the user journey, from signing up to finding a roommate. '''sql

+-----------------------+ | Sign Up / Login | +-----------------------+ | v +-----------------------+ | Complete Profile | | (Personal Details) | +-----------------------+ | v +-----------------------+ | Set Preferences | | (Budget, Lifestyle) | +-----------------------+ | v +-----------------------+ | Swipe Through Matches| +-----------------------+ | v +-----------------------+ | Match and Chat | +-----------------------+

'''

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App Routines

1. User Registration Routine

• The user will sign up with an email, phone number, or social media account.
• Upon registration, the user is prompted to complete their profile (personal information, budget, preferences, etc.).

2. Profile Matching Routine

• A user's preferences (budget, location, personality, etc.) will be analyzed and matched against others in the database.
• The algorithm calculates a compatibility score, which is used to rank roommate suggestions.

3. Swipe-to-Match Routine

• Users can swipe through potential roommates, similar to Tinder’s swiping mechanic.
• If two users swipe right (accept), a match is made, and they can begin chatting.

4. Messaging Routine

• Once matched, users can message each other to discuss the living situation, schedule meetings, and further assess compatibility.

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Datasets

To enable smooth matching and enhance user experience, we require the following datasets:

• User Profiles: Data including location, budget, lifestyle preferences, and personal details.

  • Sample Dataset:

  {
    "user_id": 123,
    "location": "San Francisco",
    "budget": 1500,
    "preferences": ["clean", "night owl", "pets"],
    "personality": "outgoing"
  }

• Room Listings: Data about available rooms and apartments (price, location, amenities).

  • Sample Dataset:

  {
    "room_id": 456,
    "location": "San Francisco",
    "price": 1200,
    "amenities": ["wifi", "laundry", "parking"],
    "available_from": "2023-05-01"
  }

• User Matching Preferences: Preferences for roommate matching (likes, dislikes, lifestyle).

  • Sample Dataset:

  {
    "user_id": 123,
    "ideal_roommate": {
      "budget": 1300,
      "lifestyle": "quiet",
      "cleanliness": "high",
      "pet_preference": "none"
    }
  }

• Swipe Data: Tracks swipes (right/left) for users to help calculate compatibility scores.

  • Sample Dataset:

  {
    "user_id": 123,
    "swiped_right": [124, 125, 126],
    "swiped_left": [127, 128]
  }

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Roommate Matching Logic

The roommate matching logic works through a scoring system based on user preferences and profile data.

1. Compatibility Scoring

• Compatibility is based on shared attributes such as cleanliness, lifestyle, budget, and pet preferences.
• Each attribute is assigned a weight, and the system calculates an overall compatibility score between two users.

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Next Steps

1. Reviews: Allow Users to see ratings and reviews from previous roommates. Use reliability scores (like Uber ratings) to highlight trustworthy users.
2. Integrated Expense Tracker: Built-in tool to split rent, utilities, and grocery bills automatically. Optional integrations with Venmo, PayPal, or direct bank transfers
3. Verification + Safety Features: ID verification, social media linkage, and optional background checks. Users can report suspicious behavior and block others.
4. Smart Alerts + Map-Based Search: Users can set up alerts for new rooms within a specific budget and neighborhood. Real-time map allows users to compare commute times, nearby transit, and local amenities.

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