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Machine Learning and Artificial Intelligence in Travel — Part2

Face recognition

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.

Sentiment analysis

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.

Customized dataset

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.

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

ByteBridge, a data labeling tooling platform with real-time workflow management, providing a flexible data training service for the machine learning industry.

Data quality guarantee

ML-assisted capacity can help reduce human errors by automatically pre-labeling

The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.

Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.

All results are thoroughly assessed and verified by a human workforce and machine

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

Communication Cost Saving

On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.

ByteBridge Labeling Instruction Template

Control Your Own Project — 2D Images Labeling

In addition, researchers can create the data project by themselves, upload raw data, download processed results, check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and take control over the project status.

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.

We can provide personalized annotation tools and services according to customer requirements.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.


A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.


If you need data labeling and collection services, please have a look at, the clear pricing is available.

Please feel free to contact us:




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A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. —

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