Summary of Enhancing Monotonic Modeling with Spatio-temporal Adaptive Awareness in Diverse Marketing, by Bin Li et al.
Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in Diverse Marketing
by Bin Li, Jiayan Pei, Feiyang Xiao, Yifan Zhao, Zhixing Zhang, Diwei Liu, HengXu He, Jia Jia
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposes a novel Constrained Monotonic Adaptive Network (CoMAN) method for spatio-temporal perception in marketing pricing, tackling challenges in allocating limited budgets and predicting user responses to incentives. CoMAN captures preferences through two modules, learning convexity and concavity as well as expressing sensitivity functions. This approach enhances the allocation of incentive investments during pricing, increasing conversion rates and orders while maintaining budget efficiency. The proposed method outperforms state-of-the-art methods in offline and online experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create a new way to help companies decide how much money to spend on marketing campaigns. They want to make sure the company gets the most bang for its buck! To do this, they develop a special tool called CoMAN that can learn about people’s preferences in different locations and at different times. This helps the company understand what people like and don’t like, so they can make better decisions. The results show that CoMAN is really good at helping companies make more money while spending less. |
