dc.citation.conferencePlace |
US |
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dc.citation.endPage |
5786 |
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dc.citation.startPage |
5771 |
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dc.citation.title |
International Conference on Machine Learning |
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dc.contributor.author |
Choi, Young-Geun |
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dc.contributor.author |
Kim, Gi-Soo |
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dc.contributor.author |
Choi, Yunseo |
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dc.contributor.author |
Cho, Wooseong |
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dc.contributor.author |
Paik, Myunghee Cho |
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dc.contributor.author |
Oh, Min-Hwan |
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dc.date.accessioned |
2024-01-09T16:05:09Z |
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dc.date.available |
2024-01-09T16:05:09Z |
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dc.date.created |
2024-01-09 |
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dc.date.issued |
2023-07-23 |
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dc.description.abstract |
Contextual dynamic pricing is a problem of setting prices based on current contextual information and previous sales history to maximize revenue. A popular approach is to postulate a distribution of customer valuation as a function of contextual information and the baseline valuation. A semi-parametric setting, where the context effect is parametric and the baseline is nonparametric, is of growing interest due to its flexibility. A challenge is that customer valuation is almost never observable in practice and is instead type-I interval censored by the offered price. To address this challenge, we propose a novel semi-parametric contextual pricing algorithm for stochastic contexts, called the epoch-based Cox proportional hazards Contextual Pricing (CoxCP) algorithm. To our best knowledge, our work is the first to employ the Cox model for customer valuation. The CoxCP algorithm has a high-probability regret upper bound of Õ(T |
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dc.identifier.bibliographicCitation |
International Conference on Machine Learning, pp.5771 - 5786 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/67921 |
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dc.language |
영어 |
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dc.publisher |
ML Research Press |
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dc.title |
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2023-07-23 |
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