Abstract. Collaborative Filtering (CF), the most commonly-used technique for recommender systems, does not make use of object attributes. Several hybrid recommender systems have been proposed, that aim at improving the recommendation quality by incorporating attributes in a CF model.In this paper, we conduct an empirical study of the sensitivity of attributes for several existing hybrid techniques using a movie dataset with an augmented movie attribute set. In addition, we propose two attribute selection measures to select informative attributes for attribute-aware CF filtering algorithms.