Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User Preferences
This work addresses travel destination recommendations for users on e-commerce sites, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of recommending travel destinations by comparing offline algorithms to a production system in online A/B tests at Booking.com, finding that a Naive Bayes-based ranker significantly increased user engagement over the current system.
Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system.