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We study the problem of scoring and selecting content-based features for a collaborative filtering (CF) recommender system. Content-based features play a central role in mitigating the "cold start" problem in commercial recommenders. They are also useful in other related tasks, such as recommendation explanation and visualization. However, traditional feature selection methods do not generalize well to recommender systems. As a result, commercial systems typically use manually crafted anddoi:10.1145/2507157.2507203 dblp:conf/recsys/RonenKZN13 fatcat:wt5desdcdvha3mk4hlozgfpbi4