Chef Dalle: Transforming Cooking with Multi-Model Multimodal AI

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

I am pleased to confirm that the article has greatly improved. It has been reorganized, providing also a clear answer to the points raised in the earlier review. I have appreciated the enhanced description of the framework (i.e. the workflow) and especially the demo attached, which summarises how Chef Dalle works. 

I think the article is suitable for publication.

Author Response

Thank you very much for your positive review.

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

 

 

The document presents a promising concept for a recipe recommendation system called Chef Dalle. However, there are some aspects that, if addressed, could improve the quality of the paper:

• Clarify what makes Chef Dalle distinct from other existing solutions, highlighting its unique features and advantages.
• Include user studies or real-world impact metrics concerning user feedback, as no data or analysis is provided.
• The paper lacks a rigorous evaluation methodology and empirical data to support the claims of the system's effectiveness. It relies heavily on theoretical potential and mentions user feedback without providing concrete evidence. Including a quantitative analysis of user feedback, comparative studies against existing platforms, or a detailed evaluation of the system's accuracy would enhance its scientific soundness.

 

 

Author Response

Clarify what makes Chef Dalle distinct from other existing solutions, highlighting its unique features and advantages.

Incorporated a section detailing Chef Dalle’s distinctive capabilities such as its multimodal input, dynamic recipe generation, and multi-API integration, which distinctly set it apart from traditional platforms like Yummly, BigOven, and SuperCook.

Include user studies or real-world impact metrics concerning user feedback, as no data or analysis is provided.

Implemented a two-day user study with 20 participants during a social event to collect actionable feedback and measure Chef Dalle's performance in real-world settings.

The paper lacks a rigorous evaluation methodology and empirical data to support the claims of the system's effectiveness. It relies heavily on theoretical potential and mentions user feedback without providing concrete evidence. Including a quantitative analysis of user feedback, comparative studies against existing platforms, or a detailed evaluation of the system's accuracy would enhance its scientific soundness.

Strengthened the paper by integrating quantitative and qualitative analyses based on user feedback. Utilized data from embedded feedback systems within recipe cards to refine algorithms and enhance system accuracy. Also, conducted comparative studies against major platforms, highlighting Chef Dalle’s superior personalization and user engagement capabilities

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

While the paper presents an interesting idea, the execution falls short due to a lack of technical novelty and a reliance on established methods for the core recommendation system. This presents a missed opportunity, as the potential for a valuable solution exists.
The proposed solution leverages state-of-the-art deep learning models for conversation and vision, which is a promising approach. However, the recommendation system itself, which is arguably the core functionality, relies on well-established methods and lacks significant novelty.
Without a substantial revision to the core recommendation system, introducing a more technically novel approach, this submission cannot be recommended.

Comments on the Quality of English Language

NA

Author Response

Please find our answer attached.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article introduces "Chef Dalle," a multimodal AI-driven recipe recommendation system designed to enhance personalized cooking experiences. Chef Dalle integrates advanced technologies such as voice-to-text conversion via Whisper, ingredient image recognition through GPT-Vision, and recipe image generation using DALL-E 3. The system employs TF-IDF vectorization and cosine similarity to tailor recipe suggestions based on users’ dietary preferences and restrictions.

I liked very much the way the authors have organized the paper and I generally enjoyed reading it. The language is clear and, as far I can see, the usage of English language and grammar is proper. The references quoted seem well-chosen to support the article's claims about the capabilities and innovation of the Chef Dalle system.

I have some comments:

• Perhaps, the article lacks empirical data or user study results to substantiate claims about user satisfaction and the system's effectiveness. Future work could provide valuable insights into actual user engagement and the system's impact on dietary habits.

• Moreover, although the system architecture and AI models are discussed, the article could benefit from deeper technical details on the implementation challenges (i.e. the handling of diverse and complex dietary restrictions or - even more interesting - the scalability of the system).

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• Finally, The discussion on the broader implications for public health and ethical considerations is present, but it is kind of short. In my opinion, expanding on these topics could strengthen the paper's impact and relevance, particularly in addressing potential biases in AI models and privacy concerns related to user data (which the authors mention).

In conclusion, the article can be accepted considering the above points.

 

Author Response

Please find our answer attached.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The authors introduced an application suggesting recipes fit for dietary conditions. The application leverages advantages of multimodal models for merging voice and images. In the paper, the authors explained what tool they used, and goal of the project. One thing is, base methodologies and technologies the authors combined and implemented are well developed ones, so it is difficult to say it is novel.

Author Response

Please find our answer attached.

Author Response File: Author Response.pdf