Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916385931657216 |
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| author | Nishimura, Naoki Kobayashi, Ken Nakata, Kazuhide |
| author_facet | Nishimura, Naoki Kobayashi, Ken Nakata, Kazuhide |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11039 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation Nishimura, Naoki Kobayashi, Ken Nakata, Kazuhide Machine Learning Artificial Intelligence 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. |
| title | Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.11039 |