# Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation

arXiv: https://arxiv.org/abs/2407.11039 · HTML: https://mlsanity.com/paper/2407.11039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [More Robust Doubly Robust Off-policy Evaluation](https://mlsanity.com/paper/1802.03493/) (2018)
- [Offline A/B testing for Recommender Systems](https://mlsanity.com/paper/1801.07030/) (2018)
- [Balanced Policy Evaluation and Learning](https://mlsanity.com/paper/1705.07384/) (2017)
- [Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection](https://mlsanity.com/paper/2008.09982/) (2020)
- [Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation](https://mlsanity.com/paper/2311.18207/) (2023)
- [Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions](https://mlsanity.com/paper/2207.14798/) (2022)
- [Robust portfolio optimization model for electronic coupon allocation](https://mlsanity.com/paper/2405.12865/) (2024)

## Built on by (most cited first, 1 of 1)

- [When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide](https://mlsanity.com/paper/2608.12489/) (2026)

## Abstract (arXiv)

Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate revenue and exploring alternative policies to collect data for future policy improvement via off-policy evaluation (OPE). To balance this trade-off, we propose a novel approach that combines a model-based revenue maximization policy and a randomized exploration policy for data collection. Our framework enables flexible adjustment of the mixture ratio between these two policies to optimize the balance between short-term revenue and future policy improvement. We formulate the problem of determining the optimal mixture ratio as multi-objective optimization, enabling quantitative evaluation of this trade-off. We empirically verified the effectiveness of the proposed mixed policy using synthetic data. Our main contributions are: (1) Demonstrating a mixed policy combining deterministic and probabilistic policies, flexibly adjusting the data collection vs. revenue trade-off. (2) Formulating the optimal mixture ratio problem as multi-objective optimization, enabling quantitative evaluation of this trade-off.