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短期租赁动态定价中的人机协作上下文赌博者：历史热身与审批门控实时学习的结构等价性Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

一项新的研究论文提出了人机协作门控赌博者（HITL-GB）框架，旨在改善短期租赁市场的动态定价策略。该框架允许人类代理验证上下文赌博算法生成的价格建议，有效地将人类判断融入定价过程。通过利用历史数据，HITL-GB框架旨在减少在线学习场景中常见的冷启动问题，从而提高定价决策的效率和准确性。

A new research paper presents the Human-in-the-Loop Gated Bandit (HITL-GB) framework, which aims to improve dynamic pricing strategies in the short-term rental market. This framework allows human agents to validate price recommendations generated by a contextual bandit algorithm, effectively integrating human judgment into the pricing process. By utilizing historical data, the HITL-GB framework seeks to reduce the cold-start problem commonly faced in online learning scenarios, thereby enhancing the efficiency and accuracy of pricing decisions.

要点

  1. HITL-GB框架将人类验证整合到算法定价策略中。

    The HITL-GB framework integrates human validation into algorithmic pricing strategies.

  2. 通过利用历史数据解决动态定价中的冷启动问题。

    It addresses the cold-start problem in dynamic pricing by leveraging historical data.

  3. 这种方法可能显著改善短期租赁市场的定价结果。

    This approach could significantly improve pricing outcomes in the short-term rental market.

为何重要

这项研究强调了将人类专业知识与机器学习算法结合以优化定价策略的重要性，这可以带来更好的市场表现和客户满意度。

This research highlights the importance of combining human expertise with machine learning algorithms to optimize pricing strategies, which can lead to better market performance and customer satisfaction.

按谁的角度看

产品经理

对于产品经理而言，理解HITL-GB框架可以为开发结合人类洞察的定价工具提供参考，可能导致市场上更具竞争力的定价策略。

For product managers, understanding the HITL-GB framework can inform the development of pricing tools that incorporate human insights, potentially leading to more competitive pricing strategies in the market.

开发者

开发者可以利用这项研究的见解构建更强大的定价算法，包含人类反馈循环，从而增强其系统的适应性和有效性。

Developers can leverage the insights from this research to build more robust pricing algorithms that include human feedback loops, enhancing the adaptability and effectiveness of their systems.

学生与求职者

数据科学和机器学习的学生及求职者可以从这项研究中学习到人类反馈在算法决策中的整合，这是在不断发展的AI应用领域中一项宝贵的技能。

Students and job-seekers in data science and machine learning can learn from this research about the integration of human feedback in algorithmic decision-making, a valuable skill in the evolving landscape of AI applications.

覆盖来源

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