5 research outputs found
Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User Preferences
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.Comment: 6 pages, 2 figures in SIGIR 2015, SIRIP Symposium on IR in Practic
Where to go on your next trip? Optimizing travel destinations based on user preferences
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