Machine Learning and Artificial Intelligence in Travel — Part2
Another artificial intelligence technology that is popular in travel is facial recognition.
Facial recognition software can recognize or verify a person’s identity by capturing, analyzing, and comparing facial patterns. It uses artificial neural networks to process biometric data and generate filters to convert facial details in the image into digital features. The system then compares these features with the database to determine similarity.
For example, many airports worldwide have begun to use facial recognition technology to enable tourists to pass check-in and review documents faster and more efficiently. JetBlue uses facial recognition technology to achieve a paperless boarding experience. The airline cooperated with the U.S. Customs and Border Protection (CBP) to install fully integrated biometric self-boarding gates at some U.S. airports, including New York’s Kennedy International Airport (JFK).
Personalization can be the most valuable application of artificial intelligence in the travel and hospitality industry to generate recommendations. 47% of consumers said that artificial intelligence-based promotions based on past purchases would improve their experience. If hotels provide this service, 26% of consumers will visit more frequently.
Like Amazon or Netflix’s standard recommendations, many online travel agencies, airlines, and hotels apply machine learning algorithms to analyze data, build sophisticated recommendation engines, and automatically provide tailored recommendations.
Social media and travel commenting platforms have become very influential in recent years. A report showed that 86% of people are interested in a particular travel destination after having browsed other users’ online posts. About 60% of online users will look for ideas on Facebook or Instagram.
Since customers tend to write about their travel experience, brands can use this valuable information to improve their services and provide better offers. As of 2020, TripAdvisor alone has 884 million user opinions and comments. It is impossible to process such a large amount of data manually. Here, machine learning techniques (sentiment analysis ) can be used to quickly and effectively analyze brand-related reviews. Many travel-related companies have used sentiment analysis to track social media reactions to their products and services.
With the acceleration of the commercialization of AI and the application of AI technologies in all walks of life, the expectation of data quality in special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.
If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
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