Matrix approximation is one of the most effective methods for collaborative filtering-based recommender systems. However, the high computation complexity of matrix factorization on large datasets limits its scalability. Prior solutions have adopted co-clustering methods to partition a large matrix into a set of smaller submatrices, which can then be processed in parallel to improve scalability. The drawback is that the recommendation accuracy is lower as the submatrices only contain subsets of the user-item rating information. This paper presents WEMAREC, a weighted and ensemble matrix approximation method for accurate and scalable recommendation. It builds upon the intuition that (sub)matrices containing more frequent samples of certain user/item/rating tend to make more reliable rating predictions for these specific user/item/rating. WEMAREC consists of two important components: (1) a weighting strategy that is computed based on the rating distribution in each submatrix and applied to approximate a single matrix containing those submatrices; and (2) an ensemble strategy that leverages user-specific and item-specific rating distributions to combine the approximation matrices of multiple sets of co-clustering results. Evaluations using real-world datasets demonstrate that WEMAREC outperforms state-of-the-art matrix approximation methods in recommendation accuracy (0.5-11.9% on the MovieLens dataset and 2.2-13.1% on the Netflix dataset) with 3-10X improvement on scalability.