Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
Authors: Yanghao Xiao, Haoxuan Li, Yongqiang Tang, Wensheng Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three public datasets validate our method achieves state-of-the-art performance in the presence of hidden confounding, regardless of RCT data availability. (...) 4 Experiments |
| Researcher Affiliation | Academia | Yanghao Xiao1,3 Haoxuan Li2 Yongqiang Tang3, Wensheng Zhang4, 1University of Chinese Academy of Sciences 2Peking University 3Institute of Automation, Chinese Academy of Sciences 4Guangzhou University |
| Pseudocode | Yes | Algorithm 1: The Proposed Meta Debias Learning Algorithm |
| Open Source Code | No | The paper states 'The paper provides open access to the data and code' in its NeurIPS checklist, but does not provide a specific link or explicit instruction for accessing the code within the main paper content or appendix that the user can directly act on. |
| Open Datasets | Yes | we conduct extensive experiments on three public datasets, COAT2, YAHOO! R33, and KUAIREC4 [11]. 2https://www.cs.cornell.edu/~schnabts/mnar/ 3https://webscope.sandbox.yahoo.com 4https://github.com/chongminggao/Kuai Rec |
| Dataset Splits | Yes | Following previous works [3, 28, 29, 34], we randomly split 5% unbiased data from the test set as validation set, and for all methods requiring RCT data, we employ observational data without hidden confounding to pretend RCT data. |
| Hardware Specification | Yes | All the methods are implemented on Py Torch with Adam as the optimizer and NVIDIA A40 as the computing resource, and we tune learning rate in {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05} and weight decay in [1e-7, 10]. |
| Software Dependencies | No | All the methods are implemented on Py Torch with Adam as the optimizer and NVIDIA A40 as the computing resource, and we tune learning rate in {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05} and weight decay in [1e-7, 10]. It mentions 'Py Torch' but does not specify a version number. |
| Experiment Setup | Yes | Two-layer multi-layer perceptron are used as the base model, and we compare proposed methods with both RCT data-free and RCT data-based methods. (...) All the methods are implemented on Py Torch with Adam as the optimizer and NVIDIA A40 as the computing resource, and we tune learning rate in {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05} and weight decay in [1e-7, 10]. |