Sequential Recommendation with Probabilistic Logical Reasoning
Authors: Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Lei Zhao
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiments on various sequential recommendation models demonstrate the effectiveness of the SR-PLR. Our code is available at https://github.com/Huanhuaneryuan/SR-PLR. |
| Researcher Affiliation | Academia | 1Soochow University 2Suzhou Vocational University 3 Macquarie University 4 Rutgers University 5 Texas Tech University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 1 is a framework diagram. |
| Open Source Code | Yes | Our code is available at https://github.com/Huanhuaneryuan/SR-PLR. |
| Open Datasets | Yes | Experiments are conducted on three publicly available datasets, Amazon Sports, Toys [He and Mc Auley, 2016] and Yelp. |
| Dataset Splits | Yes | Following previous works [Kang and Mc Auley, 2018], we use the 5-core version for all datasets and adopt the leave-one-out method to split these three datasets. |
| Hardware Specification | Yes | We run all methods in Py Torch [Paszke et al., 2017] with Adam [Kingma and Ba, 2015] optimizer on an NVIDIA Geforce 3070Ti GPU |
| Software Dependencies | No | The paper mentions "Py Torch", "Adam optimizer", and being "implemented based on Rec Bole", but it does not specify exact version numbers for these software components. |
| Experiment Setup | Yes | The batch size and the dimension of embeddings d are set to 2048 and 64 in our experiments. The max sequential length for all baselines is set as 50. We train all models 50 epochs. ... SR-PLR is trained with a learning rate of 0.002. For the logic network, we set the λ in Eq. (12) as a hyperparameter and select it from [0, 1] with step 0.1. For the negative item number in Eq. (6), we choose it from 1 to 10. |