Movie Recommender System

Work in Progress 👷 ⛑️

This repository is version 2 of my initially project. I want to take it to next level by build a better recommendation system and more engaging for the community.

Description

This project is a web-based movie recommender system designed to suggest movies to users based on their preferences. It allows new users to search for their favorite movies and, based on their selections, recommends similar movies. Users can like or dislike these recommendations, refining the system's future suggestions. Additionally, users can generate a unique, shareable link of their favorite and recommended movies.

Features

• Search Functionality: New users can search for their favorite movies.
• Recommendation Engine: Recommends movies similar to the user's favorites.
• Feedback System: Users can like or dislike recommendations to refine future suggestions.
• Shareable Lists: Users can create and share links to their curated movie lists.

Tech Stack

• Backend: Python, Flask
• Frontend: React/Vue.js (optional)
• Database: MongoDB/PostgreSQL
• Machine Learning: Scikit-learn, Pandas, NumPy, TensorFlow/PyTorch
• Hosting/Scaling: AWS/Google Cloud Platform

Building the Recommender System

1. Collaborative Filtering: This is a common approach where the system makes recommendations based on the preferences of similar users. For instance, if User A likes the same movies as User B, then the movies liked by User B, but not yet seen by User A, can be recommended to User A.
2. Content-Based Filtering: This approach uses item features to recommend additional items similar to what the user likes, based on their previous actions or explicit feedback.
3. Hybrid Approach: Combining both collaborative and content-based filtering can often provide more accurate recommendations.

Implementation Steps

1. User Interface:
   • Develop a search functionality for new users to find their favorite movie.
   • Implement a system to capture likes and dislikes on the recommended movies.
2. Backend Logic:
   • When a user selects a favorite movie, use your recommender system to fetch similar movies.
   • Update recommendations based on user feedback (likes/dislikes).
3. Recommender System:
   • Use movie datasets (like the MovieLens dataset) to train your model.
   • Implement a model using collaborative filtering, content-based filtering, or a hybrid approach.
4. Shareable Link Creation:
   • Generate unique links for user-curated lists that can be shared externally.
   • Implement appropriate routing in your Flask app to handle these links.
5. Scaling:
   • Use caching mechanisms like Redis to store frequently accessed data to speed up recommendation generation.
   • Implement efficient data structures and algorithms to handle large datasets.
   • Consider using distributed computing frameworks like Apache Spark for handling and processing large datasets in a scalable way.
6. Testing and Deployment:
   • Thoroughly test the application for both functionality and performance.
   • Deploy the application using a cloud service provider.
7. Continuous Improvement:
   • Implement analytics to understand user behavior.
   • Continuously train your model with new data to improve recommendations.

Considerations for Large-Scale Deployment

1. Efficient Data Storage and Retrieval: As the number of movies and users grows, efficiently storing and retrieving data becomes crucial. Using scalable databases and indexing strategies is essential.
2. Distributed Computing: For handling millions of movies and user interactions, distributed computing frameworks like Apache Spark can be used. They allow for processing large datasets in parallel across multiple nodes.
3. Load Balancing and Auto-scaling: Implementing load balancers and auto-scaling policies ensures that the system can handle varying loads without compromising on performance.
4. Caching Strategies: Implementing caching for frequently accessed data can significantly reduce load times and improve user experience.
5. Real-time Data Processing: For real-time recommendation updates, consider technologies like Apache Kafka or AWS Kinesis for streaming data processing.
6. Machine Learning Model Optimization: Optimize ML models for speed and efficiency. Consider using approximate nearest neighbors (ANN) algorithms for faster similarity searches in large datasets.