Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
<|reference_start|>Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation: 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.<|reference_end|>
@article{nishimura2024balancing,
    title={Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon
  Allocation},
    author={Naoki Nishimura, Ken Kobayashi, and Kazuhide Nakata},
    journal={arXiv preprint arXiv:2407.11039},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11039},
    primaryClass={cs.LG cs.AI}
}