AI Case Study

Expedia optimises its best fare search results using machine learning

Expedia has built its core business, that of delivery best fare results in real time, based on machine learning. With flight itineraries and schedules constantly changing, the company uses learning algorithms to adjust their search results.

Industry

Consumer Goods And Services

Travel And Leisure

Project Overview

"One weekend, the team at Expedia let BFS run for two full days on a single query: a round trip between Seattle and Atlanta in the United States. When they got back on Monday the algorithm had delivered "quadrillions of results," says Fleischman, VP of global product.

This algorithm is always being tested and tweaked by the machine learning team at Expedia. The data scientists will test the algorithm against a whole range of bias, such as towards a business traveller or a family.

'We test multiple versions of the algorithm against one another and tweak the tuning on that,' says Fleischman. 'We try these bias against one another and look at the result sets and ask if you get a better set.'

How do they define success, then? 'We test and look at metrics to see if people bought more flights because we gave them a better result,' Fleischman explains."

Reported Results

Best fare results

Technology

Function

Operations

General Operations

Background

As" flight itineraries and schedules are constantly changing, Expedia's proprietary 'best fare search' (BFS) has to 'learn' and adapt all the time.

The extent of the problem can be summed up by one statistic. The average Expedia.com flight search will take three seconds to deliver results. In those three seconds you will see, on average, 16,000 flight options, in order of convenience or price or time."

Benefits

Data

flight itineraries and schedules