Download or read book Artificial Intelligence Big Data Travelling Consumption Prediction written by Johnny Ch LOK and published by . This book was released on 2019-07-06 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: For another example, in 2016 year , Apple computer revamped its travelling scene photos app to allow travelling consumers to search for specific travelling destinations in the travelling scene phots, they want to find anywhere travelling destination photos, not just dates and locations. Each travelling photo that an intelligent phone or intelligent pad user takes goes through 11 billion computations, so that travelling scene photos can understand exactly where is the travelling destination photography to let online travelling consumer to feel anywhere they plan to go to the location to travel. So, (AI) learning machine can make online travelling photos more attractive to influence potential travelers choose to the destination to travel after they see the travelling destination scene photos from internet.It seems that in future, (AI) machine learning will allow online travelling search to evolve even further. Search engineers will deliver refined recommendations to airlines' online traveler e-ticket search users and use less human input to predict travelling consumers' needs from internet channel. For IBM computer example, it indicated 90% of the data that exists today has been created in the last two years.This huge explosion of past traveler's e-ticket consumption data gives the opportunity to quickly spot and react to the latest trends, fashion and fads among its travelling clients and potential clients. This will allow airline or travel agent companies to better engage with younger travelling consumers, who gain influence access to the latest travelling destination and package trends.They associate with to help define who they are as individuals. Thus, travelling company brands have to identify and make use of them before travelling consumers move on, but the vast quantity of past e-ticket purchase dataavailable makes from internet channel. This a resource-intensive task. For next example, Lesara, a based online clothes store, uses this machine learning to inform its product decision often gathering information from internal and external sources.When its trends -spotting shoes. Lesara has a range of over 20 styles and sells hundreds of pairs a day. It focus on giving consumers, the very latest trends allow Lesara to develop on average of 50,000 new items each year. It compared to 11,000 old items each year. Thus, travelling agents or airlines can attempt to apply (AI) big data gathering method to gather all past e-ticket purchase data, concerns where they prefer to choose to go to the destinations to travel and what travelling packages are the most attractive to the travelers to choose to buy. It aims to help them to predict where future travelers will prefer to choose to go to travel or what travelling package they will prefer to choose to buy next year.