Summary of Balancing Immediate Revenue and Future Off-policy Evaluation in Coupon Allocation, by Naoki Nishimura et al.
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
by Naoki Nishimura, Ken Kobayashi, Kazuhide Nakata
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 combines model-based revenue maximization and randomized exploration for coupon allocation, balancing short-term revenue and future policy improvement via off-policy evaluation. A novel framework is introduced, allowing flexible adjustment of the mixture ratio between these two policies to optimize the balance between immediate revenue and future data collection. The problem is formulated as multi-objective optimization, enabling quantitative evaluation of this trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers have found a way to make coupon allocation more efficient by combining different strategies. They tested this approach using computer-generated data and showed that it can improve the balance between making money now and collecting information for later. |
Keywords
* Artificial intelligence * Optimization
