]]> Methods and Procedures: This project developed a comprehensive movie recommendation system utilizing collaborative filtering techniques, implemented with a Python FastAPI backend and a Remix.js frontend. The system employs the Singular Value Decomposition (SVD) algorithm, trained on the MovieLens 32M dataset, which contains 162,541 users and 59,047 movies. The architecture integrates multiple data sources, including IMDb metadata through the OMDb API, implements RESTful API endpoints for recommendation generation, and provides a modern web interface for user interaction. The system was deployed on Heroku with MySQL database hosting on the Railway platform. You can visit the recommender system by yourself on the following URL: www.salihrogo.me
Results: Comprehensive evaluation demonstrated solid performance across key metrics: Mean Absolute Error of 0.82, indicating good predictive accuracy, Hit Rate of 58.7% showing effective recommendation relevance, and catalog coverage of 72.3% ensuring adequate movie variety. The system achieved 86.4% user coverage, minimizing cold start problems, while maintaining a diversity score of 0.612 and a novelty score of 0.578, indicating balanced recommendations between popular and lesser-known content. Testing suite comprising 43 test cases validated system reliability across unit, integration, and end-to-end scenarios.


Conclusion: The implemented movie recommender system successfully addresses the content discovery challenge through effective collaborative filtering, demonstrating production-ready performance with clear pathways for future enhancement. The system provides a scalable foundation for personalized movie recommendations while maintaining data integrity, security, and user experience standards.]]>