Comparative Analysis of User-Based and Item-Based Collaborative Filtering Using the MovieLens Dataset

Abstract: In today's Now, recommender systems in the present time become an essential tool to screen out relevant content for users. This paper constitutes a comparative study of user-based and item-based collaborative filtering (CF), using MovieLens dataset. Methodology, the details of implementation, and evaluation on metrics RMSE, Precision@K, Recall@K, and Coverage are described. A working web prototype of the recommendation system is presented, with screenshots demonstrating its functioning. Results showed that Item-Based CF has more stability and accuracy for large recommendation tasks while User-Based CF still captures dynamic user similarities.

Keywords: Recommender Systems; Collaborative Filtering; MovieLens; User Based CF; Item Based CF; Evaluation Metrics.