Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
KitcheNette: Predicting and Ranking Food Ingredient Pairings using Siamese Neural Network
Authors: Donghyeon Park, Keonwoo Kim, Yonggyu Park, Jungwoon Shin, Jaewoo Kang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As the results demonstrate, our model not only outperforms other baseline models, but also can recommend complementary food pairings and discover novel ingredient pairings. |
| Researcher Affiliation | Academia | Donghyeon Park, Keonwoo Kim, Yonggyu Park, Jungwoon Shin and Jaewoo Kang Korea University EMAIL |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/dmis-lab/Kitche Nette |
| Open Datasets | Yes | In this work, we utilized Recipe1M [Marin et al., 2018], a dataset containing approximately one million recipes and their corresponding images which were collected from multiple popular websites related to cooking. |
| Dataset Splits | No | The paper mentions 'Validation' in Table 3 for performance metrics, implying a validation set was used, but it does not provide specific details on the split percentages or counts for the validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python Scikit-learn [Pedregosa et al., 2011] package' but does not provide specific version numbers for Scikit-learn or any other software dependencies. |
| Experiment Setup | Yes | We train our proposed model to minimize the loss function (Mean Squared Error) which can be expressed as follows: Θ(yab Yab)2 where L is the computed loss function to be minimized during training, Θ are the model parameters to be trained, yab is the true score value, Yab is the predicted score value, and N is the total number of input pairs used for training. We use the Adam optimizer for our model. |