Artificial Intelligence Predicts Traveller Behaviors?

By JOHNNY CH LOK and Johnny C.H. Lok

Artificial intelligence will use this past travelling destinations and travelling package information to power predictive search results, e.g. predictive future travelling consumer's choice behavioral processing for where will be their preferable travelling destination choice and how to design travelling package to satisfy future travelling clients' needs.Predictive search will improve the quality of online travelling search results, and provide new insights into travelling consumers' travellingdestination and package behavior and the moments which matter to them. Search will give recommendation into tailored how travelling consumer individual travelling destination choice in travelling decision making process. Several of the largest online platforms already use (AI) travelling machine learning to improve predictive travelling consumer behavioral search results.For example, Google's rank brain technology adds research by understanding the context in which the travelling consumer has entered it. Over time, rank brain will learn further from user behaviors Amazon's DSSTNE ( pronouned destiny) learns from shoppers' purchasing habits and consumption behavior to offer better product recommend actions, which Amazon can offer before a consumer has entered anything into the search bar.Such as (AI) big data can gather past online travelers' e-ticket purchase transactions to conclude that online traveler's travelling choice habits and online traveler consumption behavior to offer better travelling destinations andtravelling package opinions to travel agents or airlines. However, this technology is not independent of human input. For example, Google engineers will periodically retain the rank brain system to improve the models it uses.

• Format: Paperback
• Language: English
• ISBN: 1079015345
• ISBN13: 9781079015348
• Release Date: July 2019
• Publisher: Independently Published
• Length: 189 Pages
• Weight: 1.38 lbs.
• Dimensions: 0.5" x 8.5" x 11.0"