Maximizing profit in uplift modeling through regret-optimal policy learning strategies

Inhaltsverzeichnis (Table of Contents)

• Introduction
• Research Contributions
• Structure of this Paper
• Preliminaries
• Uplift Modeling in Machine Learning
• Profit Maximization in Uplift Modeling
• Policy Learning and multi-armed Bandit Models
• Reinforcement Learning for Uplift Modeling
• Policy Learning Approaches to Uplift Modeling
• Multi-Armed Bandit Models for Uplift Modeling
• Related Literature
• Profit Maximization through Uplift Modeling
• Reinforcement Learning for Uplift Modeling
• Experiment
• Methodology
• Data Sets
• Empirical Results
• Regret-Optimality as the reward metric for PL

Frequently Asked Questions

What is Uplift Modeling in marketing?

Uplift Modeling predicts the incremental impact of an action (like a marketing campaign) on an individual's behavior compared to no action.

How does Reinforcement Learning (RL) differ from Uplift Modeling?

While Uplift Modeling uses labeled data to predict effects, RL is an iterative technique that aims to find optimal decision policies directly.

What is profit maximization in this context?

It involves targeting only individuals who react favorably to a treatment while accounting for revenue potential and the specific costs of the action.

What are Multi-Armed Bandit models?

They are a framework for policy learning that balances exploring new actions and exploiting known effective actions to maximize rewards.

What is "Regret-Optimality"?

It is a reward metric used in policy learning to measure how much profit was lost by not making the optimal decision in every case.