Improving Sequential Recommendation Consistency with Self-Supervised Imitation
Authors: Xu Yuan, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye Ding
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four real-world datasets show that SSI effectively outperforms the state-of-the-art sequential recommendation methods. |
| Researcher Affiliation | Collaboration | Xu Yuan1,2,3 , Hongshen Chen3 , Yonghao Song1 , Xiaofang Zhao1 and Zhuoye Ding3 , 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China 3JD.com, China |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using 'real-world datasets from Amazon review datasets' and selects four subcategories, but it does not provide a specific URL, DOI, or a formal citation to the exact dataset version used or instructions on how to access it. |
| Dataset Splits | Yes | We hold out the last two interactions as validation and test sets for each user, while the other interactions are used for training. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory details used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam optimizer' but does not provide specific version numbers for these or other software dependencies required to replicate the experiment. |
| Experiment Setup | Yes | The hyper-parameters are set as λ1 = λ2 = λ3 = 1. We use the Adam optimizer[Kingma and Ba, 2015] with a learning rate of 0.001, where the batch size is set as 256 in the teacher and student model, respectively. |