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Titlebook: Artificial Intelligence and Machine Learning in the Travel Industry; Simplifying Complex Ben Vinod Book 2023 The Editor(s) (if applicable)

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發(fā)表于 2025-3-21 18:38:03 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Intelligence and Machine Learning in the Travel Industry
期刊簡稱Simplifying Complex
影響因子2023Ben Vinod
視頻videohttp://file.papertrans.cn/163/162246/162246.mp4
發(fā)行地址Includes case studies of successful innovation in organisations.Addresses why AI has been less quickly adopted in the travel industry.Explores technological innovation in the travel industry
圖書封面Titlebook: Artificial Intelligence and Machine Learning in the Travel Industry; Simplifying Complex  Ben Vinod Book 2023 The Editor(s) (if applicable)
影響因子.Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high.?.The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice..
Pindex Book 2023
The information of publication is updating

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Applying reinforcement learning to estimating apartment reference rents,nts adapt. The proposed RL model is trained and tested against real-world datasets of reference rents that are estimated with the use of one rules-based approach by two leading apartment management companies. Empirical results show that this RL-based approach outperforms the rules-based approach with a 19% increase in RevPAU on average.
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Installing into an Existing Treeze forecast, and market share estimation. We also describe methodologies based on Machine Learning algorithms that can use to forecast these quantities and explain how they can be leveraged to improve pricing and revenue management practices.
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Management and Monitoring Toolslearn traveler’s booking patterns and the latent progression of the booking curve. This solution can be leveraged by independent hoteliers in their revenue management strategy by comparing their behavior to the market.
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Ausgew?hlte Aspekte aus weiteren Studienupon masses of data to systems that learn on the fly with little data, and from (b) centralized (even if in the cloud) machine learning to distributed artificial intelligence, and from (c) recommender systems to marketplace approaches.
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