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At Booking.com, we recommend destinations to travelers who are not yet sure where to go. Typical recommender systems rely on past user feedback to recommend items to users. This can be problematic in our real-world application, where user interactions are infrequent (sparse data), and where many users come in for the first time or change interest over time (continuous cold start problem). Here, we propose to use the current user situational context, instead of past user interactions, to inform recommendations. In an A/B test on Booking.com users, contextual recommendations increased user engagement by 20%.
Clik here to view.
At Booking.com, we recommend destinations to travelers who are not yet sure where to go. Typical recommender systems rely on past user feedback to recommend items to users. This can be problematic in our real-world application, where user interactions are infrequent (sparse data), and where many users come in for the first time or change interest over time (continuous cold start problem). Here, we propose to use the current user situational context, instead of past user interactions, to inform recommendations. In an A/B test on Booking.com users, contextual recommendations increased user engagement by 20%.
