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The proposed algorithm is limited to matrix factorization model, and can be hardly extended to more state-of-art neural network-based latent factor models proposed in recent years. Because the derivate of model parameters with respect to training labels in Equation (7) needs to be a closed form solution as in matrix factorization. This may restrict a broader impact of the proposed solution. I'm concerned about the authors' claim on the trade-off between personalization and accuracy. As emphasized in the title, the authors consider the performance gain of the proposed algorithm as trading personalization for accuracy, but there is no direct empirical evaluation evidence to support this claim.

The paper received overall very positive scores (after communication through author response). All the reviewers agree that the paper made a very interesting contribution from a novel angle to understand the tradeoff between "over-personalization" and accuracy. The empirical results provide convincing support for the claim. I suggest the authors incorporate the feedback from the reviewers in the revision.

Most of the tools in the Humphrey suite are generative AI models - in this case, technology which takes large amounts of information and summarises it in a more digestible format - to be used by the civil service. Among them is Consult, which summarises people's responses to public calls for information. The government says this is currently done by expensive external consultants who bill the taxpayer "around 100,000 every time." Parlex, which the government says helps policymakers search through previous parliamentary debates on a certain topic, is described by The Times as "designed to avoid catastrophic political rows by predicting how MPs will respond". Other changes announced include more efficient data sharing between departments.

Online dating is one of the surest and quickest ways to meet someone. The possibilities of finding what you're looking for are as wide as the internet search. Sometimes you find who you want, but for more and more women, swiping left has exposed them to untold dangers. Increasingly, online dating has become a space where women are being exposed to sexual violence and abuse. This week on Now You Know we talk to Jackie Cruz, a sexual violence researcher, about how women can stay safe on dating apps.

The authors motivate the proposed model with the setting in which items have "true" but unobserved labels/ratings and the observed labels/ratings given by evaluators are potentially incorrect. This differs from the very common problem in recommendation systems or collaborative filtering where evaluators provide their subjective ratings but there is not assumed to be any "true" rating (e.g., users of Netflix giving 1-5 star ratings to movies). This seems like a common but underexplored setting that is worthy of further study within machine learning. The authors are also right to highlight interpretability as a desired aspect of any machine learning solution that may yield post-hoc insights into common human biases and thus suggest corrective measures. This paper does a good job of motivating the proposed model and situating it within the crowdsourcing and human annotation literature.

First of all, the problem considered in this paper is interesting and useful to some potential applications that require the true rating matrix not influenced by any recommender systems. However, the inference of the true rating matrix from the observed one is an ill-posed problem, which need the "strong" (somewhat unrealistic) assumptions. The questions about the assumptions are summarized as follows: 1. Assumption 1 is quite restricted in the sense that the popular recommendation algorithms (e.x. Bayesian matrix factorization) cannot be properly expressed in Eq. (2). If the real-world RS makes use of a complex recommendation algorithm that is not covered by the Assumption 1, it is hard to validate the quality of the true rating matrix extracted by the proposed algorithm. I think that this is also non-realistic situation.

The paper explores an important topic – adversarial machine learning. While the paper contributes interesting results, it seems slightly lacking in novelty/depth. In general, the paper is well presented. Both the attack models and strategies are clearly derived and explained. It is indeed important to have this kind of analysis to fully understand the vulnerability of collaborative filtering schemes.

The authors do a good job of presenting the high-level ideas behind their contribution and presenting the relevant literature in context. The actual contribution is quite technical in nature, but a good amount of effort is taken to walk the reader through it. The authors might also look into approaches like SLIM (Ning and Karypis), which also approach matrix completion tasks using (fairly simple) models that overcome the low-rank assumption of typical matrix completion approaches. Although the paper promises to recover matrix data generated by a quite general class of functions, I struggled to understand which of the operating assumptions (section 2) are actually realistic. In particular, assumption (e) (each entry is observed independently) is certainly violated in the netflix and movielens datasets where the "missing at random" assumption does not hold (as would be the case in any dataset where users self-select what to evaluate; see papers on the "missing not at random" assumption).

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is often difficult to collect it is important to develop effective models that take advantage of the more widely available implicit feedback. We introduce a probabilistic approach to collaborative filtering with implicit feedback based on modelling the user's item selection process. In the interests of scalability, we restrict our attention to tree-structured distributions over items and develop a principled and efficient algorithm for learning item trees from data.