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 and selected features. This work presents a framework for automated selection of informative content-based features, that is independent of the type of recommender system or the type of features. We evaluate on recommenders from different domains: books, movies and smart-phone apps, and show effective results on each. In addition, we show how to use the proposed methods to generate meaningful features from text.