Feedback

Your article provides a solid analysis of collaborative filtering models and effectively addresses factors like training and testing errors, overfitting, and the impact of hidden features on performance. However, one aspect that could be explored further is the discussion on the potential reasons behind the observed discrepancy in testing error despite the varying number of hidden features. Are there specific patterns or characteristics in the testing data that may contribute to this outcome? delving deeper into the nature of the testing set could provide additional insights into the model's behavior and may offer suggestions for refining the collaborative filtering approach. This could enhance the completeness of your analysis and provide readers with a more nuanced understanding of the factors influencing model performance.