October Assignment Feedback
I found your article very interesting. I also found the math behind the models easy to follow which was good.
The first piece of feedback I have is about the adding biases paragraph. I don't understand the sentence "the average rating is 3.5 but this movie is better than average". How does the model decide which movies are actually their average rating vs which movies deserve a higher average rating?
The second piece of feedback I have is to make sure you cite the two papers you are referencing in the body of your paper. This would help the reader keep track of which models come from which paper and how the two papers are connected to each other.
Overall I thought your paper was well-written and easy to follow!
In this evaluation, it is essential to address several critical points to enhance the clarity, accuracy, and completeness of the discussed content. First and foremost, the papers under discussion must be properly cited in the references section at the end of the text. This is a fundamental scholarly practice that ensures due credit is given to the original authors and facilitates further reading for interested individuals. Furthermore, it is imperative to explicitly reference any figures used in the text to avoid any potential misinterpretation of their origin. By doing so, readers can clearly discern the source of the figures and differentiate between original content and external references.
Additionally, maintaining consistent notation is paramount in clarifying the relationships between variables in matrix factorization explanations. Specifically, the interplay between 'q,' 'p,' 'u,' and 'm' should be clearly defined. Furthermore, any modifications in the "Modifying MF Calculations" section should be distinctly differentiated from the practice of merely fixing one row or column of matrices. Addressing these concerns will result in a more precise and reader-friendly discussion of matrix factorization for recommender systems. Finally, the article should provide more explicit and thorough explanations for several key concepts, such as "adding biases," "implicit feedback," "temporal dynamics," and "confidence levels." These terms require a more comprehensive introduction to ensure readers fully understand their relevance and implications in the context of recommender systems. By addressing these points, the article will offer a more complete and informative discussion of the topic.