Kati8/ICLR_2023_2025 huggingface dataset:
authors: Mustafa Zaki
authors: Junyuan Lin
authors: Mandy Barrett Korpusik
reviews: 
Comments: The authors present a novel approach to providing food recommendations that are both healthy and tasty for customers. The proposed solution combines the power of Collaborative Filtering (CF) with a semantic-based similarity score for the food recommended in the first stage (CF). This, combined with a final health score, should give users the possibility to explore new food alternatives that they enjoy but which are also healthy.

The solution is straightforward: SVD can be used in the first stage, followed by a BERT-based similarity score for ingredients and food nutrition in the second stage, alongside a novel health score.

The idea is interesting, and the authors also conducted an A/B test. The main issue here is the lack of comparisons with previous works, for instance (but not just limited to) Pecan (2020). I strongly suggest the authors consider ways to perform quantitative comparisons with experiments (not online experiments, offline with a dataset would be ok), ideally using the same datasets as prior studies where possible. While a qualitative assessment was included, a clear quantitative comparison is needed to understand the benefits of the proposed solution.

Additionally, further insights are needed: which recommendations were effective, which were not, and why? How can these be improved? An A/B test that only compares different CF methods does not sufficiently add value to the assessment of the work.
Strengths: - An impactful problem, they are working on healthy food recommendations 
- Straightforward solution, simple and well described. The paper is easy to follow
Weaknesses: - No comparisons. They have to compare previous works w.r.t. proposed solutions, a quantitative comparisons would help to understand the real benefits of this solution 
- No baselines, really the weakness is around the fact that the authors should spend sometime trying to compare their solution
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 3
reviews: 
Comments: This paper addresses the food recommendation issues by considering food nutrition beyond collaborative preferences. The collaborative preferences are captured by two Collaborative Filtering methods (SVD and EASE). At the same time, the food nutrition scores are computed via two authors-defined empirical equations (Healthy Contribution and Unhealthy Contribution). Experiments are conducted on a filtered COCO Nutrition Database with 11.5K foods. A human evaluation with 22 users is also conducted to compare the two SVD and EASE models.
Strengths: S1: It is nice to consider the characteristics of food items, i.e., the food nutrition, when recommending the food items to users.
Weaknesses: W1: The core of healthy nutrients and unhealthy nutrients (Equations 7-8) is not well motivated. Why the four kinds of nutrition (Protein, Dietary Fibre, Vitamin, Potassium) are defined as healthy while the other three (Sugar, Sodium, Saturated Fats) are unhealthy? 

W2: The case of healthy and unhealthy is much varied with different genders and ages (old, young). The universe definition in Equations 7-8 might be inappropriate, especially the fixed weights.

W3: The evaluation metrics on recommendation should include ranking metrics like AUC, NDCG, and Recall. The RMSE metric only is not enough in Section 4.1 AUTOMATIC EVALUATION.

W4: Key details are missing in evaluation and implementation. What are the statistics of the datasets, how many users, how many interactions, how to split the training set and test set, how many words and tokens are in the food item descriptions, and how long of the average length of the food item descriptions, what is the K in K-means, any visualization e.g. t-SNE on the clustering foods?
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 2