Abstract: It is known that the models obtained via the existing ranking algorithms completely come from training data. Because much useful information on the models cannot be gotten from training data, the models are not usually accurate enough. Aiming at the above shortcoming, this paper proposes a ranking algorithm based on latent variables. Firstly, the algorithm uses structural support vector machine as learning tool, and introduces other useful information, except for training data, into algorithm framework as latent variables. On the basis, it defines an object function orienting NDCG. Because the object function is nonconvex and nonsmooth, this paper utilizes the concaveconvex procedure to approximation, and makes use of proximal bundle method to optimizing computing. Experimental results on the benchmark datasets show that the obtained model via the proposed algorithm is more precise than those only via training data.