It seems to be the main conversation starter at various industry events, workplace hallway interactions, and dinner parties. However, AI relies on the ability to interact successfully \u2013 or intelligently \u2013 with a consumer in some way. And in order to do that, you must have a robust system that understands what an input means.<\/p>\n
In travel, these options are nearly endless. If someone says in casual conversation, \u201cI want to go to Paris,\u201d we make an assumption that they mean Paris, France instead of Paris, Illinois. Similarly, \u201cShow me a flight on Friday\u201d likely means for the next Friday on the calendar instead of one random Friday next month.<\/p>\n
At the heart of this understanding is Natural Language Processing (NLP) or the task of turning travelers\u2019 common questions, those they would ask a friend or travel agent, into searchable content. We\u2019ve been experimenting with NLP for the past few years; ever since we realized that the standard travel search framework doesn\u2019t work as well on mobile devices. For example, did you know that a search request for a big city like London can turn up thousands of hotel results? Going through that information on a desktop can be challenging, but someone searching on a tablet or a mobile phone is unlikely to scroll through every page of those results. And with wearables exploding on the market, you can imagine how the number of acceptable results becomes even smaller.<\/p>\n
Filters become a very important part of slicing search data into relevant, digestible chunks of information that you care about. With mobile devices, finding an easy way to input those filters is a key priority. Selecting filters manually is an option, which is why we offer solutions like that on our mobile app and mobile websites. But the appeal of voice- and chat-bots is they take some of that filtering away by having a more fluid conversation with customers. Instead of checking a box that says, \u201cpet-friendly\u201d you could tell a chatbot you want to bring your dog, and it can narrow results to those that accept pets.<\/p>\n
Experiments available today<\/strong><\/p>\n In addition to the mounds of traveler data we look at en masse to predict trends and identify patterns, we are testing ways NLP can expedite the search process. Some of this happens on the backend of our site via machine learning and automation of certain big data tasks. But others are more obvious and available for direct interaction:<\/p>\n\n
![\"Search](\"http:\/\/blog.expedia.com\/wp-content\/uploads\/2016\/06\/Search-Anything_Expedia-1-family-friendly.png\")
<\/a><\/p>\n\n
<\/a><\/p>\n\n
<\/a><\/p>\n\n