Recent years have seen a revival of interest in the Alternating Direction Method of Multipliers (ADMM), due to its simplicity, versatility, and scalability. As a first order method for general convex problems, the rate of convergence of ADMM is O(1/k) [4, 25]. Given the scale of modern data mining problems, an algorithm with similar properties as ADMM but faster convergence rate can make a big difference in real world applications. In this paper, we introduce the Accelerated Alternating Direction Method of Multipliers (A2DM2) which solves problems with the same structure as ADMM. When the objective function is strongly convex, we show that A2DM2 has a O(1/k2) convergence rate. Unlike related existing literature on trying to accelerate ADMM, our analysis does not need any additional restricting assumptions. Through experiments, we show that A2DM2 converges faster than ADMM on a variety of problems. Further, we illustrate the versatility of the general A2DM2 on the problem of learning to rank, where it is shown to be competitive with the state-of-the-art specialized algorithms for the problem on both scalability and accuracy.