Collaborative Filtering Implemented in MATLAB

This article presents collaborative filtering algorithms implemented in MATLAB, which play a crucial role in recommendation systems. Collaborative filtering is a user behavior-based recommendation algorithm that analyzes user preferences and historical interactions to accurately predict and recommend content of potential interest. These implementations not only provide working code but also demonstrate deep understanding and research into recommendation system mechanics. During implementation, several factors must be considered, including data scale, algorithm efficiency, and prediction accuracy. The MATLAB code typically involves key functions for matrix operations (using built-in functions like svd() for matrix factorization), similarity computation (using cosine similarity or Pearson correlation), and prediction generation. Algorithm optimization techniques are incorporated to enhance recommendation accuracy and real-time performance, such as dimensionality reduction methods and parallel computing approaches. Ultimately, collaborative filtering serves as a core component in recommendation systems, significantly contributing to improved user experience and platform revenue optimization.