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 to travel to. Since then, research has demonstrated that price is one of many factors that make a place attractive. 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.