0561bc7ecba98e39ca7994f93311ba23-AuthorFeedback.pdf

We thank the reviewers for thoughtful feedback. "researchers working on pairwise comparisons and preference learning should find this paper to be interesting and Furthermore, we note that we also plan to make our code available as soon as the review period concludes. In our derivation, we pose the problem in a noiseless environment only for simplicity. For similar reasons, we also did not compare our method against algorithms utilizing different models of preference. As with any recommender system, practical considerations are important.