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..
Sequential Recommendation with Probabilistic Logical Reasoning
Authors: Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Lei Zhao
IJCAI 2023 | Venue PDF | 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. |