In today's technological landscape, recommender systems provide essential personalized suggestions by leveraging user preferences. This study evaluates User-Based (UBCF) and Model-Based Collaborative Filtering (MBCF) on the MovieLens 1M dataset, comparing performance on complete data versus partitions based on age and occupation. Using MAE and RMSE metrics, we assessed UBCF with Euclidean/Cosine similarity and MBCF with NMF/SVD. Results show MBCF with SVD achieved the best performance (MAE: 0.6909, RMSE: 0.8761), outperforming UBCF by approximately 5.2% in MAE and 5.4% in RMSE. This confirms model-based approaches, particularly SVD, excel with complete datasets, while demographic partitionin... More >