Summary of Benchmarking For Deep Uplift Modeling in Online Marketing, by Dugang Liu and Xing Tang and Yang Qiao and Miao Liu and Zexu Sun and Xiuqiang He and Zhong Ming
Benchmarking for Deep Uplift Modeling in Online Marketing
by Dugang Liu, Xing Tang, Yang Qiao, Miao Liu, Zexu Sun, Xiuqiang He, Zhong Ming
First submitted to arxiv on: 1 Jun 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 paper introduces a standardized benchmark for deep uplift modeling (DUM), a technique used in online marketing to identify user groups for targeted incentives. The authors provide a unified evaluation protocol and conduct extensive experiments on two industrial datasets with different preprocessing settings, re-evaluating 13 existing DUM models. The results show that recent advancements in DUM do not differ significantly from traditional approaches in many cases, highlighting the limitations of DUM in generalization. The paper aims to facilitate fair comparisons between new and existing models, providing valuable insights for researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes online marketing more effective by creating a standard way to test different models that help businesses decide who gets special deals. It looks at 13 different models used in this field and finds that newer models don’t always do better than older ones. This means that businesses might not need to spend as much time or money on new models. The paper also shows where these models can go wrong, which is important for making good decisions. |
Keywords
» Artificial intelligence » Generalization
