In recent years, most recommender systems rely on collaborative filtering (CF) based on matrix factorization (MF) that can predict unknown ratings by completing a rating matrix. However, this approach cannot be used for the cold start where no rating information is available for a given user or item. To address this problem, we develop a new hybrid CF (HCF) technique incorporating CF with content information. The proposed HCF is based on an auto-encoder (AE) consisting of a nonlinear encoder and a linear decoder. This type of AE is called the basis learning AE (BAE), because it can learn the basis of the row space of a sparse input matrix by its encoder. In the proposed scheme, the input to the BAE is a content augmented rating matrix; the BAE learns the basis of the row space of a given rating matrix, which is a subset of the basis of the content augmented rating matrix, and recovers each row of the rating matrix by a linear combination of the learned basis. Unlike most existing HCF schemes, our model does not incorporate additional content-based objective terms; yet extensive experiments on real-world datasets show that the proposed HCF can significantly advance the state-of-the-art.