Collaborative filtering (CF) is the state-of-the-art approach to item recommendation. However, it can neither recommend new items with no user feedbacks, nor could it recommend "long-tail" items easily. Content-based filtering can solve both problems through content analysis. However, content-based filtering alone has a much worse performance than CF. In this paper, we fuse user feedbacks and content analysis into the probabilistic matrix factorization framework. In particular, we propose a recursive dynamic programming approach to computing item similarity matrix from item content. Item latent factors are predicted from the item similarity matrix when no usage data is available. We investigate how performances of recommendation algorithms vary on items with different popularities. Results show that our approach has better performance than the same hybrid model with naive item similarity measures and Matrix Factorization.