🎬 Moviemeter – Personalized Movie Recommendation Web App
Moviemeter is a full-stack web application that lets users discover, review, and discuss movies — while also receiving personalized recommendations based on their preferences. It’s powered by a custom-built content-based recommender system using Python, scikit-learn, and MongoDB.
🚀 Features
👤 Authentication
Secure user signup & login system
Authenticated access to personalized content
❤ User Interactions
Like & favorite movies
Write reviews and view others’ opinions
Engage in threaded movie discussions
🧠 Personalized Recommendations
Content-based recommender using:
Movie vectors (genres, metadata)
Cosine similarity with user profile vector
Built with Python, pandas, and scikit-learn
🌐 Tech Stack
Frontend: React, Tailwind CSS
Backend: Express.js for app APIs, Flask for ML API
Database: MongoDB (with Mongoose ODM)
AI/ML: Python, Pandas, Scikit-learn
Deployment: Deployed on [Railway/Render/Vercel/etc.]
📂 Folder Structure
moviemeter/ │ ├── frontend/ # React.js frontend ├── backend/ # Express.js API backend ├── recommender/ # Flask-based ML microservice │ ├── final_df_data.pkl.gz │ ├── movies_data.pkl │ └── recommender.py ├── .env # Environment variables └── README.md
⚙ Setup Instructions
- Clone the Repository
git clone https://github.com/sushil-sagar05/moviemeter.git cd moviemeter
- Setup Frontend (React.js)
cd frontend npm install npm run dev
- Setup Backend (Express.js)
cd ../backend npm install npm run dev
- Setup ML Recommender (Flask)
cd ../recommender pip install -r requirements.txt python recommender.py
Ensure .env files are present in backend and recommender directories with MongoDB credentials.
🧠 How Recommendations Work
-
User likes/favorites some movies
-
Flask service fetches their liked movie vectors
-
A user profile vector is created by averaging the liked vectors
-
Cosine similarity compares this profile with all other movies
-
Top results (excluding already liked) are returned
✅ Current Status
[x] Frontend UI with user interactions
[x] Backend APIs with authentication & review handling
[x] ML model deployed via Flask API
[x] Hosted and live
[ ] (Coming Soon): Docker + CI/CD DevOps integration
👨💻 Author
Sushil Sagar B.Tech | AI/ML + Full Stack Developer | Passionate about building end-to-end intelligent applications 📫 Connect on LinkedIn: https://www.linkedin.com/in/sushil-sagar-0b4538290/ 🌐 Portfolio: Coming soon...
⭐ If you like this project
Give this repo a star ⭐ and share your thoughts. Contributions, suggestions, or feedback are always welcome!