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..
Self-supervised Graph Disentangled Networks for Review-based Recommendation
Authors: Yuyang Ren, Haonan Zhang, Qi Li, Luoyi Fu, Xinbing Wang, Chenghu Zhou
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results over five benchmark datasets validate the superiority of SGDN over the state-of-the-art methods and the interpretability of learned intent factors. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University 2Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model and algorithms using equations and textual descriptions but does not include a formally labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | Following [Shuai et al., 2022], we evaluate SGDN on the Amazon review dataset [He and Mc Auley, 2016]. |
| Dataset Splits | Yes | Each dataset is randomly split into training, validation, and test sets with a ratio of 8:1:1. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific GPU/CPU models, memory details). |
| Software Dependencies | No | The paper mentions software components like "Adam" for optimization and "BERT-Whitening" for encoding reviews, but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The hyperparameters for the baseline models are tuned according to the original paper. It is notable that we reimplement DGCF by replacing the BPR loss [Rendle et al., 2012] with MSE loss to accommodate the rating prediction task. For SGDN, we use Adam to optimize the parameters with a learning rate of 0.01. The size of embeddings d for users/items and reviews is set as 64. We choose the number of message passing layers L from {1, 2, 3}, the number of latent factors from {2, 4, 8}, and the dropout ratio from {0.7, 0.8, 0.9}. The temperature hyperparameter τ is tuned from {0.2, 0.5, 1}. The hyperparameter λ is searched from {0.01, 0.05, 0.1, 0.5}. |