Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation
Authors: Yatong Sun, Bin Wang, Zhu Sun, Xiaochun Yang, Yan Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four real-world datasets verify the generality, effectiveness, and efficiency of our proposed Bir DRec. |
| Researcher Affiliation | Collaboration | Yatong Sun1,2, Xiaochun Yang1 , Zhu Sun3 , Bin Wang1, Yan Wang2 1School of Computer Science and Engineering, Northeastern University, China 2School of Computing, Macquarie University, Australia 3Center for Frontier AI Research, Institute of High Performance Computing, A*STAR, Singapore |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/AlchemistYT/BirDRec. |
| Open Datasets | Yes | Datasets. We adopt four real-world datasets with varying domains, sizes, sparsity, and average sequence lengths shown in Table 1. Specifically, ML-1M (ML)[45] is a popular movie recommendation benchmark. Beauty (Be) [46] is the product review dataset collected from Amazon.com. Yelp (Ye) [10] is a business recommendation dataset released by Yelp.com. QK-Video (QK) [47] is a video recommendation dataset crawled from Tencent.com. |
| Dataset Splits | Yes | For each user, we preserve the last two interactions for validation and testing, while the rest are used for training. |
| Hardware Specification | Yes | All the experiments are conducted on an NVIDIA Quadro RTX 8000 GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number. |
| Experiment Setup | Yes | For all methods, Xavier initializer [51] and Adam optimizer [52] are adopted; and the best hyper-parameter settings are empirically found based on the performance on the validation set. For Bir DRec, it is implemented by Py Torch with batch_size = 1024, d = 64, learning_rate = 0.01 for Yelp and 0.001 for other datasets, L = 5, β = 0.1, β = 0.1, K = 10, and ρ = 0.9. To ensure accurate rectification with reduced ϵ, Bir DRec is trained without rectification in the first 10 epochs. |