“Analyzing User-Based and Item-Based Recommender Systems: A Comparative Examination”

Abstract

This research paper presents an in-depth analysis and comparative examination of two prominent recommender system approaches: user-based collaborative filtering and item-based collaborative filtering. Recommender systems play a pivotal role in enhancing user experiences by providing personalized recommendations. This study aims to dissect the mechanisms, strengths, and limitations of user-based and item-based methods, offering valuable insights for researchers and practitioners in the field. Through a comprehensive evaluation, we aim to shed light on the comparative effectiveness of these approaches in different scenarios and highlight considerations for their practical implementation.