MATLAB Implementation of Recommendation System with Machine Learning

This MATLAB-implemented recommendation system integrates machine learning methodologies, originally developed by Stanford University's Andrew Ng. The system provides personalized recommendation services by analyzing users' historical data and personal preferences. It employs machine learning algorithms to automatically learn and optimize recommendation models, enhancing prediction accuracy and recommendation effectiveness. The system utilizes collaborative filtering algorithms with vectorization techniques to handle large-scale user-item matrices efficiently. Key implementations include cost function optimization using advanced gradient descent methods and regularization techniques to prevent overfitting. The codebase features matrix factorization approaches that decompose user-item interaction matrices into lower-dimensional latent factor representations. Developed based on cutting-edge research and technologies from Stanford University, this recommendation system demonstrates high reliability and performance. It can be applied across various domains including e-commerce platforms, social media networks, music streaming services, and movie recommendation engines. The implementation includes modules for data preprocessing, similarity computation, and prediction generation using optimized linear algebra operations. By leveraging this recommendation system, users can more effectively discover and access content and products aligned with their interests and preferences. The MATLAB code provides clear structure for model training, evaluation metrics calculation, and real-time recommendation generation using efficient matrix operations.