Microblogging sites have emerged as major platforms for bloggers to create and consume posts as well as to follow other bloggers and get informed of their updates. Due to the large number of users, and the huge amount of posts they create, it becomes extremely difficult to identify relevant and interesting blog posts. In this paper, we propose a novel convex collective matrix completion (CCMC) method that effectively utilizes user-item matrix and incorporates additional user activity and topic-based signals to recommend relevant content. The key advantage of CCMC over existing methods is that it can obtain a globally optimal solution and can easily scale to large-scale matrices using Hazan's algorithm. To the best of our knowledge, this is the first work which applies and studies CCMC as a recommendation method in social media. We conduct a large scale study and show significant improvement over existing state-of-the-art approaches.
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