Summary of Egean: An Exposure-guided Embedding Alignment Network For Post-click Conversion Estimation, by Huajian Feng et al.
EGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click Conversion Estimation
by Huajian Feng, Guoxiao Zhang, Yadong Zhang, Yi We, Qiang Liu
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes the Exposure-Guided Embedding Alignment Network (EGEAN) to improve accurate post-click conversion rate (CVR) estimation in online advertising systems. The method addresses the Sample Selection Bias problem and Covariate Shift by aligning covariates between click and non-click spaces. Additionally, a Parameter Varying Doubly Robust Estimator is introduced to handle small propensities better. Online A/B tests on the Meituan advertising system show that EGEAN outperforms baseline models in terms of CVR and GMV. The study’s findings demonstrate the effectiveness of EGEAN for accurate CVR estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how well online ads work by better guessing what people will do after they click on an ad. Right now, there are some problems that make it hard to get this right. This research suggests a new way to solve these problems called EGEAN. It works by matching up information between people who click on ads and those who don’t. Then, it uses special math to make good guesses about what will happen after someone clicks on an ad. The results show that this method is better than other methods at getting accurate answers. |
Keywords
» Artificial intelligence » Alignment » Embedding
