Exploring collaborative filtering through K-Nearest Neighbors and Non-Negative Matrix Factorization

Abstract

Collaborative filtering (CF) algorithms have received a lot of interest in recommender systems due to their ability to give personalized recommendations by exploiting user-item interaction data. In this article, we explore two popular CF methods—K-Nearest Neighbors (KNN) Regression and Non-Negative Matrix Factorization (NMF)—in detail as we dig into the world of collaborative filtering. Our goal is to evaluate their performance on the MovieLens 1M dataset and offer information about their advantages and disadvantages. A thorough explanation of the significance of recommender systems in contemporary content consumption settings is given at the outset of our examination. We look into Collaborative Filtering's complexities and how it uses user choices to produce tailored recommendations. Then, after setting the scene, we explain the KNN Regression and NMF approaches, going over their guiding principles and how they apply to recommendation systems. We conduct an extensive investigation of KNN Regression and NMF on the MovieLens 1M dataset to provide a thorough evaluation. We describe the model training processes, performance measures, and data pre-processing steps used. We measure and analyse the predicted accuracy of these strategies using empirical studies, revealing light on their effectiveness when applied to various user preferences and content categories.