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Machine Learning and Artificial Intelligence in Travel — Part1

Imagine you are planning a trip. A few years ago, you needed to spend a lot of time and energy researching destinations and accommodation options, booking flights, and hotels, renting cars and performing a host of other travel-related activities. Today, with the help of machine learning and artificial intelligence, you can use a one-stop travel platform to plan and book everything you need.

Let’s look at the use of ML and AI in travel and the changes they bring to domain business.


Digital assistants or chatbots are among the most prominent examples of artificial intelligence applications in the travel industry. According to statistics provided by Google, one-third of international travelers are interested in using chatbots to plan and book their trips.

The services of a virtual travel assistant range from suggesting simply travel destinations to providing local weather forecasts and even booking a room/flight or renting a car. Travel chatbots are usually integrated with instant messaging platforms such as Skype, Facebook Messenger, Telegram, Slack, etc.

For example, Expedia, one of the world’s leading online travel agencies, has launched a Facebook Messenger bot to help travelers choose the right hotel and make reservations. Just enter @Expedia in the conversation field, and you can start using the robot and its guidance to choose the right hotel for a specific city and date. You may need to answer the same question several times in a row, but the robot will help book and manage the trip at the end.

Eddy Travels is another example of an AI-powered travel chatbot that can help search for flight deals, find accommodation, and get 24/7 travel inspiration. The bot has more than 200 million active users and can be used on dedicated websites and Telegram.

Travel companies continue to improve their services by integrating various intelligent assistants. Some travel chatbots can even identify and answer vague queries such as “romantic winter vacation in Europe”. In addition, their functions can go far beyond research and booking. Some chatbots can be used as mobile travel guides or companions to solve problems or provide information during travel.

Despite all the benefits, it is worth noting that chatbots cannot wholly replace human interaction.

Voice-enabled virtual assistant

Artificial intelligence solutions take the concept of a seamless hotel accommodation experience to a whole new level. A new technology called a voice-enabled virtual assistant has entered many hotels worldwide.

Guests can use tools such as Amazon Alexa to control various facilities in the hotel room. The idea is as follows: The room is equipped with various IoT devices connected to a central hub. A voice assistant controls these devices. Therefore, guests can manage many room services by simply issuing voice commands, such as adjusting bedroom lights or turning on the TV.

The hotel industry is becoming more IoT-friendly and digital. In a recent report, Oracle collected the views of 150 hotel operators, of which 78% of the respondents agreed with the large-scale use of voice assistants to control room equipment, lights, and air conditioning.

AI’s Reliance on High-quality Data

There is an important consensus in the AI industry:

The quality of the training data directly determines the performance of the final AI model. The more scalable and accurate data, the more robust algorithm will be.

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot 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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