Building a Movie Social Platform: Combining Social Media with Movie Recommendations

By Mohneesh Naidu

BTech Student at Veermata Jijabai Technological Institute (VJTI), Mumbai

Introduction

Hey there! I'm Mohneesh Naidu, a BTech student at VJTI, Mumbai. I'm excited to share my journey of building a comprehensive movie social platform that combines the best aspects of Letterboxd and Instagram, enhanced with a powerful recommendation system. This project aims to create a community where movie enthusiasts can connect, share their thoughts, and discover new films through personalized recommendations.

Project Overview

The Vision

Have you ever wished for a platform that combines the social aspects of Instagram with the movie-tracking capabilities of Letterboxd? That's exactly what this project aims to achieve. The platform allows users to:

• Track their watched movies
• Get personalized weekly movie recommendations
• Connect with fellow movie enthusiasts
• Share thoughts and reviews
• Engage in real-time discussions

Tech Stack

The project leverages modern technologies to create a robust and scalable application:

• Frontend:
  • Next.js 14 with App Router
  • TypeScript for type safety
  • Tailwind CSS for styling
  • shadcn/ui for beautiful UI components
• Backend:
  • FastAPI (Python) for efficient API endpoints
  • WebSocket for real-time features
  • PostgreSQL for data storage
  • Prisma as the ORM
• Machine Learning:
  • Python-based recommendation system
  • scikit-learn for model development
  • Jupyter notebooks for data analysis
  • Kaggle datasets for training

Technical Deep Dive

Architecture Overview

The application follows a modern microservices architecture:

1. Next.js Frontend Service
   • Server-side rendering for better SEO
   • App Router for efficient routing
   • API routes for backend communication
   • Real-time updates using WebSocket
2. FastAPI Backend Service
   • RESTful API endpoints
   • Authentication and authorization
   • Business logic handling
   • WebSocket server for chat
3. Machine Learning Service
   • Weekly recommendation generation
   • User preference learning
   • Content-based and collaborative filtering
   • Model retraining pipeline

Database Design

The database schema is designed to handle complex social relationships and movie data:

1. User Management
   • Profile information
   • Authentication details
   • Follow relationships
   • Privacy settings
2. Movie Data
   • Basic movie information
   • User ratings and reviews
   • Watch status tracking
   • Custom collections
3. Social Features
   • Posts and comments
   • Likes and reactions
   • Chat messages
   • Notifications
4. Recommendation Data
   • User preferences
   • Viewing history
   • Rating patterns
   • Genre affiliations

Key Features Implementation

1. Real-time Chat System

// WebSocket connection setup
const chatSocket = new WebSocket(CHAT_ENDPOINT);

chatSocket.onmessage = (event) => {
  const message = JSON.parse(event.data);
  updateChatHistory(message);
};

2. Movie Recommendation Engine

class MovieRecommender:
    def __init__(self):
        self.model = CollaborativeFilter()
        self.content_model = ContentBasedFilter()

    def get_weekly_recommendations(self, user_id):
        # Combine collaborative and content-based filtering
        collab_recommendations = self.model.predict(user_id)
        content_recommendations = self.content_model.predict(user_id)

        return self.hybrid_merge(collab_recommendations, content_recommendations)

3. Social Feed Implementation

interface FeedItem {
  type: 'review' | 'rating' | 'list' | 'status';
  content: string;
  user: User;
  movie?: Movie;
  interactions: Interactions;
}

const FeedComponent = () => {
  const feed = useFeed();

  return (
    <div className="feed-container">
      {feed.map(item => (
        <FeedCard key={item.id} item={item} />
      ))}
    </div>
  );
};

Challenges and Solutions

1. Real-time Performance

Challenge: Handling real-time updates for thousands of concurrent users. Solution: Implemented WebSocket connection pooling and message queuing system.

2. Recommendation Accuracy

Challenge: Creating personalized recommendations with cold start problem. Solution: Hybrid approach combining content-based filtering for new users and collaborative filtering for established users.

3. Data Consistency

Challenge: Maintaining consistency across distributed services. Solution: Implemented event-driven architecture with message queues for service communication.

Future Enhancements

1. AI-Powered Features
   • Smart movie suggestions based on mood
   • Automatic content moderation
   • Enhanced recommendation system
2. Community Features
   • Movie watch parties
   • Film club creation
   • Virtual movie discussions
3. Technical Improvements
   • Edge computing for faster delivery
   • Enhanced caching strategies
   • Mobile app development

Conclusion

This project has been an incredible journey in combining social media dynamics with movie enthusiasm. The technical challenges pushed me to explore and implement various modern technologies while creating a platform that I believe will bring movie lovers together in new and exciting ways.

Connect With Me

I'm always eager to connect with fellow developers and movie enthusiasts! You can find me at:

• GitHub: Mohneesh Naidu
• LinkedIn: Mohneesh Naidu
• Email: certainlymohneesh@gmail.com

Feel free to reach out if you'd like to contribute to the project or just chat about movies and tech!