Venue: NeurIPS 2022 Workshop TSRML

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary: In this paper, the authors argue that Lokta-Volterra dynamics can model users acting based on two balancing latent states called "drive" and "interest". They further explain that the value of $\lambda(t)$ can be influenced by a control input $p(t)$. The idea is to promote digital wellbeing by learning "breaking" policies that prompt the user to take a break (where the breaking policy is $p(t)$). Experiments are conducted on semi-synthetic data.


strengths: * Promoting digital wellbeing is an important subject.
* Modeling user dynamics using the Lotka-Volterra dynamics is interesting (I don't think I have seen this particular formulation in that context before).
* The formalization seems rigorous and correct and the paper is well-written.
weaknesses: * The workshop clearly recommends 6 pages. While 9+ pages is acceptable, I believe that the paper can be drastically shortened. The related work (while relevant) can be moved to the appendix. The introduction can be shortened.
* The paper fails to demonstrate why Lotka-Volterra dynamics are representative of actual users. The experiments look at a single dataset (MovieLens 1M). This dataset does not contain information about user taking breaks (as far as I can tell).
* Overall, the paper reads as a toy experiment without clear grounding to user studies or real recommender system.

I would strongly encourage the authors to look at more datasets and try to assess whether Lotka-Volterra dynamics (with $p(t)$ as a breaking policy) is representative of the real world.
overall_recommendation: Overall, while I am concerned about the faithfulness of the model, I did enjoy reading the paper and find it a valuable contribution to the workshop. I believe the paper could be strengthened by shortening it and really focusing on the key questions: (1) Are Lotka-Volterra dynamics representative of the real world? (2) Can a breaking policy be modeled using $p(t)$? (3) Do conclusions generalize beyond the MovieLens 1M dataset?
review_title: Interesting paper

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary: This paper proposes an interesting idea of learning a break policy to let the user more engaged in a recommendation system. The proposed behavior model is formulated with the Lotka-Volterra dynamics. The results on semi-synthetic dataset show the advantage of the method.
strengths: The proposed problem setting is interesting for a recommendation system. The theory is sound and the experiments are solid.
weaknesses: The major weakness is that the relation to the trustworthy ML workshop is unclear, because I could not find any explanation or discussions about why the proposed method is trustworthy, such as robust, fair, or private.
overall_recommendation: The proposed method is solid in solving an important problem in recommendation system, but it seems that the topic is not aligned with the workshop's topic very well.
review_title: Interesting idea with solid theoretical analysis, but the relation to trustworthiness is unclear

Meta-review: Following the unanimous recommendations from reviewers, the submission is accepted.
Decision: Accept