Recommender systems are vital to the success of online retailers and content providers. One particular challenge in recommender systems is the 'cold start' problem. The word 'cold' refers to the items that are not yet rated by any user or the users who have not yet rated any items. We propose Elver to recommend and optimize page-interest targeting on Facebook. Existing techniques for cold recommendation mostly rely on content features in the event of lacking user ratings. Since it is very hard to construct universally meaningful features for the millions of Facebook pages, Elver makes minimal assumption of content features. Elver employs iterative matrix completion technology and nonnegative factorization procedure to work with meagre content inklings. Experiments on Facebook data shows the effectiveness of Elver at different levels of sparsity.