Collaborative filtering models an experimental and detailed comparative study.

Recommender systems have become indispensable tools in various domains, such as e-commerce, entertainment, and social media, for delivering personalized user experiences. Collaborative Filtering (CF) is an essential technique in RS that leverages user similarity patterns to suggest items which align with individual preferences. This study presents an experimental comparative analysis of collaborative filtering-based recommender system methods including memory-based methods (KNN variants), model-based approaches (SVD, SVD++, co-clustering), and techniques based on neural networks (NCF, DeepFM, LightGCN). We conduct a thorough evaluation of these methods on the MovieLens benchmark datasets (100K, 1M, 25M) utilizing various metrics, such as RMSE, MAE, FCP, NDCG@10, Precision@10, Recall@10, and F1@10 Score, aiming to identify the most effective approaches and understand the advantages and disadvantages of each approach. Additionally, we provide detailed insights into the working mechanisms of each model. Our comprehensive analysis reveals the strengths and limitations of each method, offering critical insights for practitioners in selecting the most suitable recommender system technique based on specific requirements and constraints. The findings indicate that, on large datasets, neural and graph-based models achieve measurable improvements in both rating accuracy and top-k ranking tasks, with ranking gains observed upto 15%. Nonetheless, more straightforward approaches (KNN, SVD) continue to hold their ground in smaller datasets or low-resource environments because of their straightforward implementation and clarity in interpretation. The results highlight the importance of achieving a balance between computational expense, scalability, and model intricacy when choosing collaborative filtering techniques for practical implementations of recommender systems. We offer practical insights to assist professionals in selecting models that are suited to particular application needs and data attributes. This research enhances the understanding of collaborative filtering techniques and offers valuable insights for improving the performance of RS across diverse domains.