Self-supervised Graph Disentangled Networks for Review-based Recommendation
Authors: Yuyang Ren, Haonan Zhang, Qi Li, Luoyi Fu, Xinbing Wang, Chenghu Zhou
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {renyuyang, zhanghaonan, liqilcn, yiluofu, xwang8}@sjtu.edu.cn, zhouchsjtu@gmail.com |
| 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}. |