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This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework incorporates all available information by coupling together multiple learning problems and using a suitable kernel or similarity function between user-item pairs. We propose and evaluate(More)
The Netflix experience is driven by a number of recommendation algorithms: personalized ranking, page generation, similarity, ratings, search, etc. On the January 6th, 2016 we simultaneously launched Netflix in 130 new countries around the world, which brought the total to over 190 countries. Preparing for such a rapid expansion while ensuring each(More)
Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to a recommendation engine. This adds latency and neglects the opportunity of immediate personalization for a user <i>while</i> the user is navigating recommendations. (More)
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