reviews: 
Comments: The paper proposes a food recommender system that goes beyond traditional recommendation models by integrating nutritional information, leveraging semantic embeddings, and providing extensive customization options.This approach incorporates detailed nutrient data, including both beneficial (e.g., protein, vitamins) and potentially harmful components (e.g., sodium, sugars), ensuring recommendations align with users' health goals. It also uses BERT-based semantic embeddings to identify similar foods, allowing the system to suggest healthier alternatives without sacrificing user satisfaction. Moreover, the system allows users to customize their dietary priorities, such as favoring healthiness or flavor similarity, making the experience more interactive and tailored to individual goals. The main objectives include enhancing personalization, aligning recommendations with nutritional guidelines, ensuring usability, and establishing mechanisms for ongoing evaluation to refine and improve the recommendation model. Through this multifaceted approach, the system aims to promote healthier eating habits while maintaining high user satisfaction.
Strengths: The paper attempts to integrate nutritional information with user preferences in food recommendations, which is a relevant and timely area of research. By using BERT for semantic embeddings, the authors leverage recent advancements in natural language processing to enhance the understanding of food relationships. This approach allows for a more nuanced recommendation that considers both taste and health aspects, potentially bridging a gap between traditional recommendation systems and health-conscious eating.

The methodology presented is well-structured, demonstrating a thoughtful design that combines established collaborative filtering techniques (EASE/SVD) with modern embedding approaches (BERT). The systematic evaluation of nutritional scores adds a robust layer to the recommendation process, ensuring that suggestions are not only appealing but also health-oriented. 

The paper is clearly organized, with each section building logically on the previous one. The visual representation in Figure 1 provides a comprehensive overview of the system architecture, making it easier for readers to grasp the flow and interaction of components. The explanations of each step in the recommendation process are thorough, enhancing the reader's understanding of how user preferences and nutritional data are integrated.

The significance of this work lies in its potential impact on promoting healthier eating habits through personalized recommendations. By addressing the dual goals of satisfying user preferences and supporting health-related dietary choices, the system aligns with current trends in nutrition and wellness.
Weaknesses: While the paper presents a food recommender system that integrates user preferences with nutritional data, it falls short in several key areas that limit its contribution to the field. The concepts largely rehash existing methods in food recommendation systems without introducing sufficiently novel insights. Although the authors attempt to integrate collaborative filtering and contextual embeddings, both EASE/SVD and BERT-based embeddings are well-established techniques. This combination, while functional, does not significantly advance the field or offer a new perspective on food recommendation challenges.

To make this work more impactful, I suggest the authors consider recent innovative approaches in food recommendation that extend beyond collaborative filtering and embeddings. For example, multi-objective optimization frameworks, such as those used by Gao et al. (2022), optimize for both health goals and user satisfaction simultaneously. Additionally, reinforcement learning-based recommender systems that adjust recommendations based on real-time user feedback could provide a more adaptive approach than static collaborative filtering models. Moreover, incorporating multi-view learning or multi-modal embeddings (e.g., combining user activity data, time of day, and food availability) could help capture a richer, context-aware recommendation space. These techniques, documented in recent studies like Zitouni et al. (2022), might allow the system to adapt recommendations dynamically, responding to factors that influence dietary choices.

Further innovation could be achieved through novel adaptations of existing methods. For example, graph-based models that map both food similarity and nutritional content in a unified framework could provide a complex, relational approach, setting this work apart from traditional collaborative filtering. These alternative formulations would help the authors demonstrate stronger novelty, particularly if they compared the proposed system against these advanced approaches. By exploring or incorporating some of these techniques, the paper could provide a more compelling case for its system’s uniqueness and address the observed gaps in novelty. This would help strengthen its contribution to the development of food recommender systems that support healthier dietary choices.

The evaluation does not incorporate essential metrics such as Hit Rate, which is particularly important for food recommendation systems because it directly measures the system's success in presenting items the user is likely to engage with. For a system focused on promoting healthier food choices, accurately predicting relevant items is crucial to encouraging adoption and sustained user interest. Including Hit Rate as an evaluation metric would provide insight into the recommendation system's accuracy and appeal. Additionally, an ablation study is needed to assess the contributions of each component of the system, helping to clarify the impact of the collaborative filtering methods, BERT embeddings, and nutritional scoring on the overall performance. This would provide a clearer understanding of the strengths and weaknesses of the proposed approach. While the paper is structured and contains clear descriptions of the components involved, this clarity does not compensate for the lack of originality and rigorous evaluation. The overall presentation does not highlight any unique or insightful findings that would warrant further exploration or application.

The paper fails to address a substantial gap in the literature or offer a new solution that could influence the domain of food recommendations. Its contributions seem incremental rather than transformative, and the proposed system does not advance our understanding of dietary recommendation systems in a meaningful way. In conclusion, the paper does not sufficiently present novel contributions or robust evaluations that would warrant acceptance.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 2