Enhancing User-Item Matrix Using Principal Component Analysis and User Profiling Techniques for User Based Collaborative Filtering Recommender System

Abstract

Collaborative filtering recommender systems are systems that aid users in finding relevant items. However, collaborative Filtering is the most popular approach for building recommender system due to superior performance.  However, collaborative filtering approach has the many inherent problems including data sparsity. This research work presents an enhanced approach to creating a personalized movie recommendation system, leveraging the power of collaborative filtering; the research work employs a Singular Value Decomposition (SVD) algorithm to capture user preferences and item characteristics from a vast movie rating dataset, ensuring the accuracy of movie recommendations. It incorporates user profiling to comprehend user preferences in a more interpretable fashion, and Principal Component Analysis (PCA), to visualize users in a reduced 2D space. It further applies K-Means clustering that categorized users into distinct segments based on their movie preferences, by facilitating target user recommendations and analysis. The fusion of these techniques results in a sophisticated recommendation system, demonstrated through practical implementation, offering both accurate movie suggestions and insights into user clusters. The experimental evaluation results revealed that, the proposed system outperformed the existing system performance, in terms of both Mean Absolute Error (MAE) and Mean Squared Error (MRSE).