MATLAB-Based Recommendation System Source Code with Collaborative Filtering Implementation

Implementing recommendation systems in MATLAB using collaborative filtering algorithms represents a widely adopted technical approach that predicts user preferences by analyzing historical behavior data. Collaborative filtering primarily falls into two categories: User-Based Collaborative Filtering and Item-Based Collaborative Filtering. The core principle of collaborative filtering involves utilizing user-item rating matrices to identify similar users or items, thereby generating recommendations for target users. In MATLAB implementation, this process can be achieved by computing similarity measures between users (such as cosine similarity or Pearson correlation coefficient) or between items. For example, the cosine similarity between users can be calculated using MATLAB's vector operations: similarity = dot(userA_ratings, userB_ratings) / (norm(userA_ratings) * norm(userB_ratings)). Key implementation steps for building recommendation systems include data preprocessing, similarity computation, and rating prediction. During data preprocessing, developers typically handle missing values and normalize rating data using functions like fillmissing() or zscore() to improve recommendation accuracy. The similarity computation phase constructs user or item similarity matrices through efficient matrix operations, which can be optimized using MATLAB's built-in pdist() and squareform() functions for large datasets. Rating prediction involves calculating potential scores for unrated items based on similar users' ratings or similar items' scores. This can be implemented through weighted averages using the similarity matrices, where predicted_rating = sum(similarity_scores * known_ratings) / sum(abs(similarity_scores)). MATLAB's strengths lie in its powerful matrix computation capabilities and comprehensive toolbox ecosystem, enabling efficient handling of matrix operations inherent in collaborative filtering algorithms. Additionally, MATLAB provides robust data visualization functionalities through functions like heatmap() and scatter3(), facilitating performance analysis and result interpretation of recommendation systems. In practical applications, collaborative filtering algorithms may encounter challenges like data sparsity and cold-start problems. These can be addressed by implementing hybrid recommendation techniques (such as combining content-based filtering) or employing matrix factorization methods like Singular Value Decomposition (SVD) using MATLAB's svd() function. Advanced optimization may involve implementing regularization techniques and cross-validation approaches to enhance recommendation quality.