reviews: 
Comments: The paper presents a novel approach to recommend food products that could, at the same time, meet two constraints: 1) suggest accurate items according to which users like; 2) suggest healthy food. Unlike previous similar approaches, the authors propose to incorporate nutritional information within the recommendation pipeline, such as essential nutrients and possibly harmful components. Then, they also propose to adopt BERT to generate semantic embeddings to learn the relationships between different food items. Finally, the system allows to customize the user experience by letting the user decide which nutrients to take into account in the received recommendations. The overall approach consists of the following steps. First, the authors exploit two popular collaborative filtering algorithms, namely, EASE and SVD, to generate traditional food item recommendations. Second, to address health, the framework generates and injects vector embeddings (through BERT) that are used to cluster food products and find the top-k similar list of items to the ones previously recommended; this allows the user to receive alternatives. After that, the nutritional score is computed with the healthy and unhealthy nutrition contributions. Finally, the nutritional score and the similarity score (from the clustering) are combined to obtain a final recommendation score. Results of the offline evaluation showed that the SVD method could outperform EASE. In addition to this, the authors conducted online A/B testing with web applications, which showed that the EASE method was actually outperforming the SVD one.
Strengths: \+ The paper tackles an interesting aspect of food recommendation, namely, the introduction of healthy suggestions alongside the usual accurate ones

\+ The authors conducted both an offline and an online evaluation of the proposed framework
Weaknesses: \- Despite the detailed introduction and presentation of the motivations behind the work, it gets very difficult to tell how the proposed approach is different from the related literature in terms of rationales, concepts, and technical aspects

\- Some important related work is missing from the paper, here to mention a few: [i, ii, iii, iv, v, vi, vii, viii]

\- Some technical details of the proposed framework are not adequately justified (see the questions section)

\- The experimental part of the paper seems quite limited and very few evaluation dimensions are taken into account; in the offline evaluation, I would have added comparisons against some of the missing citations mentioned in the second weaknesses point

**References**

[i] Zhenfeng Lei, Anwar Ul Haq, Adnan Zeb, Md Suzauddola, Defu Zhang: Is the suggested food your desired?: Multi-modal recipe recommendation with demand-based knowledge graph. Expert Syst. Appl. 186: 115708 (2021)

[ii] Weiqing Min, Shuqiang Jiang, Ramesh C. Jain: Food Recommendation: Framework, Existing Solutions, and Challenges. IEEE Trans. Multim. 22(10): 2659-2671 (2020)

[iii] Wenjie Wang, Ling-Yu Duan, Hao Jiang, Peiguang Jing, Xuemeng Song, Liqiang Nie: Market2Dish: Health-aware Food Recommendation. ACM Trans. Multim. Comput. Commun. Appl. 17(1): 33:1-33:19 (2021)

[iv] Alain D. Starke, Cataldo Musto, Amon Rapp, Giovanni Semeraro, Christoph Trattner: "Tell Me Why": using natural language justifications in a recipe recommender system to support healthier food choices. User Model. User Adapt. Interact. 34(2): 407-440 (2024)

[v] Alessandro Petruzzelli, Cataldo Musto, Michele Ciro Di Carlo, Giovanni Tempesta, Giovanni Semeraro: Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models. RecSys 2024: 1057-1061

[vi] Mehrdad Rostami, Vahid Farrahi, Sajad Ahmadian, Seyed Mohammad Jafar Jalali, Mourad Oussalah: A novel healthy and time-aware food recommender system using attributed community detection. Expert Syst. Appl. 221: 119719 (2023)

[vii] Alain Starke, Ayoub El Majjodi, Christoph Trattner: Boosting Health? Examining the Role of Nutrition Labels and Preference Elicitation Methods in Food Recommendation. IntRS@RecSys 2022: 67-84

[viii] Alain D. Starke, Christoph Trattner: Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems. IUI Workshops 2021
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2