Summary of Deep Ensemble Shape Calibration: Multi-field Post-hoc Calibration in Online Advertising, by Shuai Yang et al.
Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising
by Shuai Yang, Hao Yang, Zhuang Zou, Linhe Xu, Shuo Yuan, Yifan Zeng
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models are crucial in e-commerce advertising, particularly when estimating the true probabilities of Click-Through Rate (CTR) and Conversion Rate (CVR). The calibration problem has been addressed through various methods that train calibrators using a validation set and apply them to correct original estimates during online inference. This paper explores novel solutions for improving calibrated estimates, which are essential for informed decision-making in e-commerce advertising. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In the world of online shopping, it’s important to accurately predict how likely people are to click on ads or make a purchase. To do this, we need to “calibrate” our predictions so they’re more accurate. This is a big problem because we can’t always test our predictions before they’re used in real-life situations. The best way to solve this problem is still unknown, but researchers have come up with some ideas that might work. |
Keywords
* Artificial intelligence * Inference * Machine learning
