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001468439 001__ 1468439
001468439 003__ OCoLC
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001468439 019__ $$a1380745158
001468439 020__ $$a9783031254567$$q(electronic bk.)
001468439 020__ $$a3031254562$$q(electronic bk.)
001468439 020__ $$z9783031254550
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001468439 0247_ $$a10.1007/978-3-031-25456-7$$2doi
001468439 035__ $$aSP(OCoLC)1380995293
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001468439 050_4 $$aG156.5.I5
001468439 08204 $$a338.4791028563$$223/eng/20230602
001468439 24500 $$aArtificial intelligence and machine learning in the travel industry :$$bsimplifying complex decision making /$$cBen Vinod, editor.
001468439 264_1 $$aCham :$$bPalgrave Macmillan,$$c2023.
001468439 300__ $$a1 online resource (vi, 182 pages)
001468439 336__ $$atext$$btxt$$2rdacontent
001468439 337__ $$acomputer$$bc$$2rdamedia
001468439 338__ $$aonline resource$$bcr$$2rdacarrier
001468439 5050_ $$a1. Special issue on artificial intelligence/machine learning in travel -- 2. Price elasticity estimation for deep learning-based choice models: an application to air itinerary choices -- 3. An integrated reinforced learning and network competition analysis for calibrating airline itinerary choice models with constrained demand -- 4. Decoupling the individual effects of multiple marketing channels with state space models -- 5. Competitive revenue management models with loyal and fully flexible customers -- 6. Demand estimation from sales transaction data: practical extensions -- 7. How recommender systems can transform airline offer construction and retailing -- 8. A note on the advantage of context in Thompson sampling -- 9. Shelf placement optimization for air products -- 10. Applying reinforcement learning to estimating apartment reference rents -- 11. Machine learning approach to market behavior estimation with applications in revenue management -- 12. Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics -- 13. Artificial Intelligence in travel -- 14. The key to leveraging AI at scale -- 15. The future of AI is the market.
001468439 506__ $$aAccess limited to authorized users.
001468439 520__ $$aOver the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice. Ben Vinod is a co-founder of Charter and Go, a dynamic offer, order management, and dispatch solution for air charter operators. He served as vice president of pricing, yield management, and reservations inventory control at American Airlines Decision Technologies (1993-1999) and was senior vice president and chief scientist at Sabre (2008-2020), focused on innovation and thought leadership in pioneering advanced solutions across the travel value chain for travel suppliers and intermediaries. He has published over 50 articles in academic and trade journals, is a member of AGIFORS, and serves on the editorial board of the Journal of Revenue and Pricing Management. .
001468439 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 2, 2023).
001468439 650_0 $$aTourism$$xTechnological innovations.
001468439 650_0 $$aArtificial intelligence$$xIndustrial applications.
001468439 655_0 $$aElectronic books.
001468439 7001_ $$aVinod, Ben,$$eeditor.
001468439 77608 $$iPrint version:$$tArtificial intelligence and machine learning in the travel industry$$z9783031254550$$w(OCoLC)1363815500
001468439 852__ $$bebk
001468439 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-25456-7$$zOnline Access$$91397441.1
001468439 909CO $$ooai:library.usi.edu:1468439$$pGLOBAL_SET
001468439 980__ $$aBIB
001468439 980__ $$aEBOOK
001468439 982__ $$aEbook
001468439 983__ $$aOnline
001468439 994__ $$a92$$bISE