Summary of Rankability-enhanced Revenue Uplift Modeling Framework For Online Marketing, by Bowei He et al.
Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
by Bowei He, Yunpeng Weng, Xing Tang, Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, Chen Ma
First submitted to arxiv on: 24 May 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 The proposed approach addresses limitations in traditional uplift modeling by introducing a novel framework for revenue uplift modeling. The authors tackle challenges such as handling continuous long-tail response distributions and optimizing ranking among individuals. They develop a zero-inflated lognormal (ZILN) loss function to regress responses, customize the modeling network, and propose tighter error bounds for ranking-related errors. The approach is validated on offline public datasets and a prominent online fintech marketing platform, demonstrating its effectiveness in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Revenue uplift modeling helps businesses identify sensitive individuals who respond well to interventions like coupons or discounts. This paper improves upon traditional conversion uplift modeling by introducing a new framework that handles continuous response distributions and optimizes ranking among individuals. The authors use a zero-inflated lognormal (ZILN) loss function to regress responses, develop tighter error bounds for ranking-related errors, and test their approach on public datasets and a real-world online platform. |
Keywords
» Artificial intelligence » Loss function
