A recommender exploration filter that matches new items to audiences by comparing predicted user satisfaction with the item's own average satisfaction raised satisfaction metrics by 40% to 50% and the recommendable corpus by 10% in a live experiment.
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Item-centric Exploration for Cold Start Problem
A recommender exploration filter that matches new items to audiences by comparing predicted user satisfaction with the item's own average satisfaction raised satisfaction metrics by 40% to 50% and the recommendable corpus by 10% in a live experiment.