Popular microblogging sites such as Tumblr have attracted hundreds of millions of users as a content sharing platform, where users can create rich content in the form of posts that are shared with other users who follow them. Due to the sheer amount of posts created on such services, an important task is to make quality recommendations of blogs for users to follow. Apart from traditional recommender system settings where the follower graph is the main data source, additional side-information of users and blogs such as user activity (e.g., like and reblog) and rich content (e.g., text and images) are also available to be exploited for enhanced recommendation performance. In this paper, we propose a novel boosted inductive matrix completion method (BIMC) for blog recommendation. BIMC is an additive low-rank model for user-blog preferences consisting of two components; one component captures the low-rank structure of follow relationships and the other captures the latent structure using side-information. Our model formulation combines the power of the recently proposed inductive matrix completion (IMC) model (for side-information) together with a standard matrix completion (MC) model (for low-rank structure). Furthermore, we utilize recently developed deep learning techniques to obtain semantically rich feature representations of text and images that are incorporated in BIMC. Experiments on a large-scale real-world dataset from Tumblr illustrate the effectiveness of the proposed BIMC method.