Loading Now

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

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

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