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..
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
Authors: Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald Metoyer, Nitesh V. Chawla
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Recipe Rec outperforms state-of-the-art methods for recipe recommendation. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Notre Dame, USA 2Department of Computer Science, Brandeis University, USA 3Department of Computer Science, University of Hong Kong, Hong Kong |
| Pseudocode | No | The paper describes the model architecture and components but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Dataset and codes are available at https://github.com/meettyj/Recipe Rec. |
| Open Datasets | Yes | Dataset and codes are available at https://github.com/meettyj/Recipe Rec. |
| Dataset Splits | No | The paper mentions a 'leave-one-out method' for evaluation and sampling negative recipes for testing, but does not provide explicit train/validation/test dataset split percentages or counts for a validation set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper provides hyperparameters for the model but does not list any specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For the proposed Recipe Rec, we set the learning rate to 0.005, the number of attention heads to 4, the hidden size to 128, the temperature τ to 0.07, λ to 0.1, node and edge dropout ratio to 0.1, batch size to 1024 and the training epochs to 100. |