Learning Cheap and Novel Flight Itineraries
As a leading travel meta search engine, Skyscanner is dedicated to provide the best flight deals available on the Internet. Towards this goal, we consider the problem of efficiently constructing cheap and novel flight itineraries resulting from combining legs from different ticket providers. We analyze the factors that contribute towards the competitiveness of such itineraries and formulate the problem of predicting competitive itinerary combinations. We consider a variety of supervised learning approaches to model the proposed prediction problem and put forward a number of practical considerations for implementing them in production.
Dima (@karamshuk) is a Senior Data Scientist at Skyscanner where his focus is on applying data mining and machine learning techniques for optimizing content caching and distribution. Prior to Skyscanner, Dima was with King's College London where he worked on analysis of BBC iPlayer (a joint project with BBC) and various social media websites (Twitter, Pinterest, Foursquare, etc.). He is an active contributor to the computer networks (Infocom, ComMag, etc.) and data mining communities (KDD, WWW, etc.). Dima's work has been featured in New Scientist, BBC News and other media outlets. He also co-founded and was a former CEO of stanfy.com. More information can be found here - https://karamshuk.github.io/.