Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Authors: Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, Yefeng Zheng
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on real-world datasets shows that our CVIB significantly enhances both shallow and deep models |
| Researcher Affiliation | Collaboration | Zifeng Wang1,Xi Chen2,Rui Wen2,Shao-Lun Huang1,Ercan E. Kuruoglu1,3,Yefeng Zheng2 1Tsinghua-Berkeley Shenzhen Institute, Tsinghua University 2Jarvis Lab, Tencent 3Institute of Science and Technologies of Information, CNR, Pisa, Italy |
| Pseudocode | Yes | Algorithm 1 Counterfactual Learning with CVIB in MNAR Data for Recommendation. |
| Open Source Code | Yes | Code is available at https://github.com/Ryan Wang Zf/CVIB-Rec. |
| Open Datasets | Yes | Yahoo! R3 Dataset [25]. This is a user-song rating dataset... Coat Shopping Dataset [30]. This dataset consists of... |
| Dataset Splits | Yes | We randomly draw 30% data from the training set for validation, on which we apply a grid search for hyperparameters to pick the best configuration. |
| Hardware Specification | No | The paper does not specify the exact GPU models, CPU models, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch [26]' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | For both the MF and NCF, we fix the embedding size of both users and items to be 4... Adam [20] is utilized as the optimizer for fast convergence during training, with its learning rate in {0.1, 0.05, 0.01, 0.005, 0.001}, weight decay in {10 3, 10 4, 10 5}, and batch size in {128, 256, 512, 1024, 2048}. For NCF, we set an additional hidden layer with width 8. Specifically for CVIB, we set the hyperparameters α {2, 1, 0.5, 0.1}, and γ {1, 0.1, 10 2, 10 3}. |