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 [1].
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. |