Movie Recommendation System
Collaborative filtering engine using SVD to suggest movies to users.
Overview
This project showcases a recommendation engine built using collaborative filtering techniques. It analyzes user-item interaction data (movie ratings) to predict and recommend new movies to users, leveraging the Singular Value Decomposition (SVD) algorithm for matrix factorization.
Challenges & Solutions
A key challenge was the sparsity of the user-item matrix. This was mitigated by using dimensionality reduction techniques inherent in SVD, allowing the model to find latent features and make more robust recommendations.
Key Features
- Real-time processing and analysis
- Scalable architecture design
- User-friendly interface
- Comprehensive documentation