Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Authors: Yewen Fan, Nian Si, Kun Zhang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive numerical experiments to demonstrate the effectiveness of the proposed meta-algorithm in both synthetic datasets using a logistic regression model and a large-scale realworld dataset using a state-of-the-art recommendation neural network. |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University 2 Mohamed bin Zayed University of Artificial Intelligence 3 University of Chicago Booth School of Business |
| Pseudocode | Yes | Algorithm 1 Variance-adjusting debiasing (VAD) method |
| Open Source Code | Yes | We open-sourced our implementation at https://github.com/tofuwen/VAD. |
| Open Datasets | Yes | We use the Criteo Ad Kaggle dataset 3 to demonstrate our method s performance. The Criteo Ad Kaggle dataset is a common benchmark dataset for CTR predictions. ... 3https://www.kaggle.com/c/criteo-display-ad-challenge |
| Dataset Splits | Yes | we use the first 15 million samples, shuffle the dataset randomly, and split the whole dataset into 85% train Dtrain, 1.5% validation-train Dval train, 1.5% validation-test Dval test, and 12% test Dtest datasets. |
| Hardware Specification | No | The paper mentions 'computational constraints' but does not provide specific details on the hardware used for experiments, such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions software like DLRM, DeepCTR, and x Deep FM, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Throughout our experiments, we use the default parameters and a SGD optimizer. ... In our method, we only need to choose one hyper-parameter S ... All results reported in Section 6 use S = 2. |