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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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

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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

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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

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Canary

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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

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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

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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

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