標(biāo)題: Titlebook: Artificial Intelligence and Machine Learning in the Travel Industry; Simplifying Complex Ben Vinod Book 2023 The Editor(s) (if applicable) [打印本頁] 作者: 自由 時間: 2025-3-21 18:38
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書目名稱Artificial Intelligence and Machine Learning in the Travel Industry讀者反饋
書目名稱Artificial Intelligence and Machine Learning in the Travel Industry讀者反饋學(xué)科排名
作者: collagenase 時間: 2025-3-21 22:42 作者: 鞠躬 時間: 2025-3-22 04:17
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.作者: 教育學(xué) 時間: 2025-3-22 07:15 作者: irreparable 時間: 2025-3-22 10:59 作者: 迫擊炮 時間: 2025-3-22 16:52
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.作者: Microaneurysm 時間: 2025-3-22 20:26
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.作者: bifurcate 時間: 2025-3-22 23:11
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.作者: 容易生皺紋 時間: 2025-3-23 03:48 作者: MIME 時間: 2025-3-23 09:31 作者: 埋葬 時間: 2025-3-23 13:08
Machine learning approach to market behavior estimation with applications in revenue management,ze 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.作者: 小溪 時間: 2025-3-23 14:38 作者: hemophilia 時間: 2025-3-23 21:00
The future of AI is the market,upon 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.作者: defibrillator 時間: 2025-3-23 23:07
Book 2023solutions 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..作者: BOAST 時間: 2025-3-24 02:31 作者: concise 時間: 2025-3-24 08:40 作者: MEN 時間: 2025-3-24 12:38
Artificial Intelligence in travel, aerospace, and health care. It has been acknowledged that while adoption of AI in the travel industry has been slow, the potential incremental value is high. This paper discusses the role of AI and a range of applications in travel to support revenue growth and customer satisfaction作者: 試驗 時間: 2025-3-24 16:24
Price elasticity estimation for deep learning-based choice models:an application to air itinerary c properties for businesses: acceptable accuracy and high interpretability. On the other hand, recent research has proven the interest of considering choice models based on deep neural networks as these provide better out-of-sample predictive power. However, these models typically lack direct busines作者: Dappled 時間: 2025-3-24 22:16
An integrated reinforced learning and network competition analysis for calibrating airline itinerartility-maximization approach. The methodology integrates a reinforcement learning algorithm and an airline network competition analysis model. The reinforcement learning algorithm searches for the values of parameters of the itinerary choice model while considering maximizing a reward function. The 作者: sphincter 時間: 2025-3-24 23:26 作者: 加強(qiáng)防衛(wèi) 時間: 2025-3-25 05:57 作者: 膽汁 時間: 2025-3-25 08:36
Demand estimation from sales transaction data: practical extensions,ction data. We present modifications and extensions of the models and discuss data preprocessing and solution techniques which are useful for practitioners dealing with sales transaction data. Among these, we present an algorithm to split sales transaction data observed under partial availability, w作者: fiscal 時間: 2025-3-25 15:36
How recommender systems can transform airline offer construction and retailing,tion in the airline industry remains in its infancy. We discuss why this has been the case and why this situation is about to change in light of IATA’s New Distribution Capability standard. We argue that recommender systems, as a component of the Offer Management System, hold the key to providing cu作者: 失敗主義者 時間: 2025-3-25 18:56
A note on the advantage of context in Thompson sampling,make suggestions tailored to each customer. This has led to many products making use of reinforcement learning-based algorithms to explore sets of offerings to find the best suggestions to improve conversion and revenue. Arguably the most popular of these algorithms are built on the foundation of th作者: 教義 時間: 2025-3-25 23:26 作者: Canary 時間: 2025-3-26 01:00 作者: slow-wave-sleep 時間: 2025-3-26 07:39
Machine learning approach to market behavior estimation with applications in revenue management,o overall market conditions and competitive landscape. Market factors significantly influence customer behavior and hence should be considered for determining optimal control policy. We discuss data sources available to airlines that provide visibility into the future competitive schedule, market si作者: xanthelasma 時間: 2025-3-26 09:01
Multi-layered market forecast framework for hotel revenue management by continuously learning markery has never been more important. In this research, a machine learning approach is applied to build a framework that can forecast the unconstrained and constrained market demand (aggregated and segmented) by leveraging data from disparate sources. Several machine learning algorithms are explored to 作者: 染色體 時間: 2025-3-26 15:28
Artificial Intelligence in travel,t decade Artificial Intelligence (AI) has seen a rapid growth in adoption across a range of industry verticals such as automotive, telecommunications, aerospace, and health care. It has been acknowledged that while adoption of AI in the travel industry has been slow, the potential incremental value 作者: Grievance 時間: 2025-3-26 20:08 作者: DUCE 時間: 2025-3-26 23:20 作者: ABHOR 時間: 2025-3-27 04:45
Artificial Intelligence and Machine Learning in the Travel IndustrySimplifying Complex 作者: Feedback 時間: 2025-3-27 07:33 作者: 同義聯(lián)想法 時間: 2025-3-27 12:27
e 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..978-3-031-25458-1978-3-031-25456-7作者: antipsychotic 時間: 2025-3-27 15:32
Management and Monitoring Toolsropean market data. The results show clear differences in price elasticity between leisure and business trips. Overall, the demand for trips is price elastic for leisure and inelastic for the business segment. Moreover, the approach is flexible enough to study elasticity on different dimensions, sho作者: Extricate 時間: 2025-3-27 21:41 作者: Expressly 時間: 2025-3-28 01:51 作者: 自愛 時間: 2025-3-28 03:35
Installing into an Existing Treely consider competitive effects and assumed that an airline’s demand only depends on their prices i.e., demand is fully dedicated to an airline (loyal). This paper develops a model to capture more realistic competitive dynamics by including both these types of customer behavior.We also develop a Bay作者: 擋泥板 時間: 2025-3-28 09:45 作者: nettle 時間: 2025-3-28 11:44 作者: Herbivorous 時間: 2025-3-28 17:04 作者: STENT 時間: 2025-3-28 19:35
Price elasticity estimation for deep learning-based choice models:an application to air itinerary cropean market data. The results show clear differences in price elasticity between leisure and business trips. Overall, the demand for trips is price elastic for leisure and inelastic for the business segment. Moreover, the approach is flexible enough to study elasticity on different dimensions, sho作者: minaret 時間: 2025-3-29 01:06 作者: 細(xì)絲 時間: 2025-3-29 04:55 作者: 怕失去錢 時間: 2025-3-29 07:54 作者: dissolution 時間: 2025-3-29 12:03
How recommender systems can transform airline offer construction and retailing,de more accurate, contextualized and personalized offers to customers. This paper contains a systematic review of the different families of recommender system algorithms and discusses how the use cases can be implemented in practice by matching them with a recommender system algorithm.作者: emission 時間: 2025-3-29 18:42
A note on the advantage of context in Thompson sampling,tion, as is the case in the traditional multi-arm bandit setup. Here, we explore a popular exploration heuristic, Thompson sampling, and note implementation details for multi-arm and contextual bandit variants. While the contextual bandit requires greater computational and technical complexity to in作者: Obituary 時間: 2025-3-29 20:20 作者: 小畫像 時間: 2025-3-30 01:02
https://doi.org/10.1007/978-3-031-25456-7marketing; travel suppliers; software; airlines; OTAs; GDSs作者: 使殘廢 時間: 2025-3-30 07:13 作者: sclera 時間: 2025-3-30 08:44 作者: 遺棄 時間: 2025-3-30 12:28 作者: Aboveboard 時間: 2025-3-30 19:39 作者: 洞察力 時間: 2025-3-30 22:16 作者: 斑駁 時間: 2025-3-31 01:07 作者: 令人不快 時間: 2025-3-31 07:43
Management and Monitoring Toolstility-maximization approach. The methodology integrates a reinforcement learning algorithm and an airline network competition analysis model. The reinforcement learning algorithm searches for the values of parameters of the itinerary choice model while considering maximizing a reward function. The
