MATLAB-Based Recommendation System Source Code
Recommendation system source code implementation in MATLAB, based on collaborative filtering algorithm with practical applications in personalized recommendation scenarios
MATLAB Implementation of Recommendation System Using Collaborative Filtering
Implementation of a movie recommendation system in MATLAB utilizing collaborative filtering matrix algorithms, with MAE validation on a large-scale dataset (943 users, 1687 movies). Features file-based data handling, clustering analysis for method comparison, and graphical result visualization with performance benchmarking.
MATLAB Implementation of Recommendation System with Machine Learning
MATLAB-based recommendation system integrating machine learning approaches, originally developed by Stanford University's Andrew Ng. Features collaborative filtering algorithms and optimization techniques for personalized recommendations.
MATLAB-Based Recommendation System Source Code with Collaborative Filtering Implementation
MATLAB source code for building recommendation systems using collaborative filtering algorithms, featuring user-based and item-based approaches with matrix computation optimizations
Collaborative Filtering Algorithm: Implementation Approaches and Technical Overview
An in-depth exploration of collaborative filtering algorithms, covering user-based and item-based approaches with code implementation insights and technical considerations.