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 itembased
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
