Cross-Cultural Recipe Transformation via Neural Network and Encoder-Based Models
recipe transformation;ingredient embeddings;ingredient replacement;cuisine embeddings;Word2Vec;BERT;SBERT;Doc2Vec;computational gastronomy
Yes
No ethics review needed.
Why did the authors choose the top 5 cuisines instead of the full list of 26 cuisines?
It would be great if the authors could have more experiments with different embedding models to select the best one.
The paper presented one approach for the recipe transformation from one regional cuisine to another by combining previously pretrained model.
This paper focused on investigating a proper technique for recipe transformation from one region to another. The authors applied existing techniques (for example, Work2Vec, Doc2Vec, and BERT) to construct such models. The paper did experiments based on the RecipeDB dataset, including 26 cuisines worldwide, but only focused on the top 05 ones. The authors also presented how they processed data and implemented different combinations of pretrained models. 
In general, the technical contributions of this paper are quite weak compared to the scope of this conference. The writing in the manuscript should be improved.
The technical contribution of the paper is weak.
Yes
No ethics review needed.
1. Could the authors specify any modifications or unique applications of the Word2Vec and BERT models that distinguish this work from typical uses of these models in other domains?
2. How do the authors plan to address the scalability of their framework to include a broader array of cuisines or more complex recipes in future studies?
3. What steps will the authors take to enhance the objectivity (reproducibility) and scalability of the qualitative evaluations of ingredient substitutions?
4. In Figure 3b, where you report the average number of ingredients replaced per successful transformation, are these averages computed only from the subset of transformations that were deemed successful? Given that BERT-based methods have much lower success rates than Word2Vec, how do you ensure that the reported averages do not introduce a sampling bias that could affect the interpretation of the models' effectiveness?
This paper addresses an interesting use case in culinary arts, highlighting how machine learning can assist in recipe adaptation and fusion across cultures, which may appeal to researchers interested in interdisciplinary applications of ML.  The paper thoroughly outlines the steps involved in the data preprocessing, model training, and evaluation and tabulates several comparative experiments that may interest the community. The authors describe the dataset, preprocessing steps, embedding generation, and clustering approach, demonstrating a solid understanding of the application pipeline. Using both Word2Vec and multiple BERT-based embeddings offers a comprehensive exploration of how established NLP models can be applied to the problem of recipe transformation. The authors advance a protocol leveraging the ingredient embeddings towards successful recipe translation events that would be useful to the community. Having identified a lack of public datasets to evaluate ingredient replacement quality, the authors have advanced a detailed evaluation framework, including metrics for successful recipe transformation and ingredient replacement.
This paper presents a creative application of natural language processing models to the culinary arts domain. This study advances a computational framework for culinary fusion, posing it as a combination of representation and metric learning problems. The utility and relevance of this work are based on a claim by the authors that a valid fusion recipe can be obtained by replacing the ingredients of a recipe from the source cuisine with counterparts from the target cuisine. In this work, a recipe is defined as an ordered set of ingredients, and recipe transformation is defined as replacing some items in the above set with “appropriate” substitutes. As envisaged by the authors, the recipe transformation framework requires 
1. a computational representation of ingredients that lends itself to distance measures and 
2. a search and replace heuristic that then iterates over each item in the source recipe and replaces it with an appropriate substitute from target cuisines. 

To this end, the authors adopt Word2Vec, Doc2Vec and BERT-based approaches for generating vector embeddings of the recipe ingredients, referencing prior works that have used these techniques in similar contexts. The authors then proffer a recipe transformation protocol that uses the distance between the vector representation of the ingredients to search and replace with appropriate substitutes iteratively. This protocol appears to be a novelty of this work. The primary focus of the experiments in this work is to compare the efficacy of different ingredient representations towards successful fusion recipe generation. To this end, the authors utilized a standard culinary dataset for training and eval. They have advanced a manual, web-search-based approach to evaluate the success of the ingredient-replacement experiments and have shared results claiming that while the simpler Doc2Vec-Word2Vec is good at clustering cuisine-wise ingredient embeddings, the encoder-based models do a better job at finding ingredient replacements.
The paper does not introduce new ML methods or innovations in embedding generation, clustering, or NLP model architecture. Instead, it applies existing models (Word2Vec, BERT, SBERT) to a specific use case. The comparative analysis of Word2Vec and BERT-based models largely reiterates their known strengths and weaknesses without offering new insights or adaptations for this specific task. 

There is limited discussion of how this method would scale or perform in the context of additional or more complex cuisines, affecting the framework's perceived robustness. The authors have restricted this study to the five most well-represented cuisines in the public dataset.

The evaluation relies on subjective metrics (Google search results) rather than objective or reproducible measures. While it is understandable that a quality measure would be subjective, this also leads to a more significant scalability problem. 

The paper does not explore why specific ingredient substitutions succeed or fail across different cuisines. It does not analyze the semantic or cultural factors influencing these outcomes, which could be valuable for similar applications.
Yes
No ethics review needed. Yes, Discrimination / bias / fairness concerns Yes, Privacy, security and safety Yes, Legal compliance (e.g., GDPR, copyright, terms of use) Yes, Potentially harmful insights, methodologies and applications Yes, Responsible research practice (e.g., human subjects, data release) Yes, Research integrity issues (e.g., plagiarism, dual submission) Yes, Unprofessional behaviors (e.g., unprofessional exchange between authors and reviewers) Yes, Other reasons (please specify below)
see weaknesses
1. The paper introduces a structured framework for transforming recipes by systematically replacing ingredients.

2. It provides a comparative analysis of Word2Vec-Doc2Vec and BERT models, showing each model’s strengths.

3. The Word2Vec-Doc2Vec model achieves high success rates in preserving the culinary essence with minimal ingredient replacements.
This paper presents a framework for transforming the culinary signature of a recipe from one cuisine to another using a clustering-based computational strategy that sequentially replaces ingredients. By comparing Word2Vec-Doc2Vec and BERT models, the study finds that neural network-based models achieve higher success in recipe transformation, with Word2Vec-Doc2Vec performing best, highlighting its effectiveness in clustering cuisine-specific ingredient embeddings.
The topics sounds very interesting. However, the current version cannot be considered as a solid ICLR submission. 

1. The presentation can be improved. The organization can be reformulated. The current version is more of a technique report. E.g., the at least related works and introduction should be made clear. And the motivation should be discussed in detail, instead of just listing what authors have done.

2. The technique novelty is poor
A comparison of neural network and encoder-based models for cross-cultural recipe transformations using clustering-based approach.
@inproceedings{
anonymous2024crosscultural,
title={Cross-Cultural Recipe Transformation via Neural Network and Encoder-Based Models},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=ZxQD6oYIOm},
note={under review}
}
Every cuisine has a culinary fingerprint characterized by its idiosyncratic ingredient composition. Transforming the culinary signature of a recipe is a creative endeavor. Traditionally, such fusion recipes have arisen from creative human interventions as a product of trial and error. Herein, we present a framework to transform the culinary signature of a recipe from one regional cuisine to another. A clustering-based computational strategy was developed, which replaces the ingredients of a recipe, one at a time, to achieve the transformation of the cuisine. We used a neural network-based Word2Vec-Doc2Vec model and three encoder-based BERT models to capture the context of an ingredient within the culinary landscape. The performance of recipe transformation strategies was evaluated by scoring their success at ‘Recipe Transformation’ and manually assessing the most frequent ingredient replacements for every fusion experiment. We observe that the encoder-based models perform better at transforming recipes with fewer ingredient replacements needed, suggesting that BERT-based models are better at providing more meaningful ingredient replacements to transform the culinary signature of recipes. The percentage of successful recipe transformations in the case of Word2Vec-Doc2Vec, BERT-Mean Pooling, BERT-CLS Pooling, and BERT-SBERT model are 99.95%, 43.1%, 41.65%, and 41.45% respectively, indicating that the neural network-based model can better cluster the cuisine-wise ingredient embeddings. On the other hand, for a successful recipe transformation, the average percentage of ingredients replaced for Word2Vec-Doc2Vec, BERT-Mean Pooling, BERT-CLS Pooling, and BERT-SBERT model are 77%, 52.3%, 51.6% and 51.5%, respectively. Our study shows a way forward for implementing cross-cultural fusion of recipes.
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recipe transformation ingredient embeddings ingredient replacement cuisine embeddings Word2Vec BERT SBERT Doc2Vec computational gastronomy
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applications to computer vision, audio, language, and other modalities
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Cross-Cultural Recipe Transformation via Neural Network and Encoder-Based Models
ICLR 2025 Conference Submission
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