368,343 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
IMPROVING RECOMMENDATION PERFORMANCE WITH USER INTEREST EVOLUTION PATTERNS
Effective recommendation is indispensable to customized or personalized services. Collaborative filtering approach is a salient technique to support automated recommendations, which relies on the profiles of customers to make recommendations to a target customer based on the neighbors with similar preferences. However, traditional collaborative recommendation techniques only use static information of customers’ preferences and ignore the evolution of their purchasing behaviours which contain valuable information for making recommendations. Thus, this study proposes an approach to increase the effectiveness of personalized recommendations by mining the sequence patterns from the evolving preferences of a target customer over time. The experimental results have shown that the proposed technique has improved the recommendation precision in comparison with collaborative filtering method based on Top k recommendation
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