In many different web services, we hear about machine learning for recommendation systems that help users tackle information overload - there are simply too many movies, songs, and books for users to usefully browse through. Travel is a little bit different - the world does not have millions of cities - but finding new, interesting places to travel to is still a challenge. Years ago, Skyscanner started it’s ‘everywhere’ search, allowing users to find the cheapest places they could travel to. Since then, research has demonstrated that price is one of many factors that make a place attractive and interesting. In this talk, I’ll discuss how we’ve bootstrapped a destination recommender system using the rich implicit data generated by Skyscanner’s millions of users, simple algorithmic approaches, and experiments that gauge how localised and personalised recommendation affects user engagement.Bootstrapping a Destination Recommender System
Dr. Neal Lathia is a senior data scientist at the Skyscanner, in London, where he focuses on designing and building a variety of products that use machine learning. Neal has a PhD in Computer Science from University College London: his PhD research focused on collaborative filtering in online recommender systems. Previously, Neal was a Senior Research Associate in the Computer Laboratory at the University of Cambridge, an honorary Research Associate in the College of Behavioral and Social Sciences at the University of Maryland, a Research Associate in the Department of Computer Science at University College London, and a visiting researcher at Telefonica Research.