MATLAB Implementation of Recommendation System Using Collaborative Filtering

This article presents a MATLAB implementation of a recommendation system using collaborative filtering matrix algorithms. The core methodology involves predicting user preferences by analyzing rating patterns across similar users and items, implemented through matrix factorization techniques. We validate system accuracy using Mean Absolute Error (MAE) metrics, calculating deviation between predicted and actual ratings. The dataset comprises 943 users and 1687 movies, handled through file I/O operations to manage large-scale data efficiently. Additional clustering analysis (using k-means or hierarchical clustering) enables comparative evaluation of different recommendation approaches. Visualization components include plotting ROC curves, precision-recall graphs, and performance comparison charts using MATLAB's plotting functions like plot() and scatter(). The implementation demonstrates data preprocessing, similarity computation using cosine/pearson correlation, and prediction generation through weighted rating aggregation.