Abstract
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.
Original language | Undefined |
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Title of host publication | Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 |
Place of Publication | New York |
Publisher | Association for Computing Machinery |
Pages | 1097-1100 |
Number of pages | 4 |
ISBN (Print) | 978-1-4503-3621-5 |
DOIs | |
Publication status | Published - Aug 2015 |
Event | 38th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 - Santiago, Chile Duration: 9 Aug 2015 → 13 Aug 2015 Conference number: 38 |
Publication series
Name | |
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Publisher | ACM |
Conference
Conference | 38th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 |
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Abbreviated title | SIGIR |
Country/Territory | Chile |
City | Santiago |
Period | 9/08/15 → 13/08/15 |
Keywords
- EWI-26682
- METIS-315156
- IR-99022