Abstract: For the problem that the model learned from traditional ranking algorithm which converts ranking problem to classification or regression is not accurate，a novel ranking algorithm is proposed.It views the ranking problem as a procedure of structured learning which learns a rank structure from the train set.The algorithm defines a object function of query level，and presents using the cutting plane algorithm to solve the problem that the algorithm has exponential number of constraints.For the sub-problem of finding the most violated constraints，the paper transforms it into a simple sorting in descending order.Experimental results on the benchmark datasets show that the algorithm proposed in this paper is more effective than the traditional ranking algorithm.

Key words: structured learning, ranking algorithm, cutting plane algorithm, Support Vector Machine（SVM）

摘要： 传统排序算法将排序问题转换成分类或回归问题来求解，这样得到的模型不够精确。对此提出一种新的排序算法，该算法把排序问题看成一个结构化学习过程，即通过训练集来学习一个排序结构。算法首先定义了一个查询级的目标函数，针对算法约束条件太多，难以直接优化，提出使用割平面算法进行求解。对于算法中的“寻找最违约排列”子问题，将其变换成为一个简单的降序排列问题。基于基准数据集的实验表明，相比起传统的排序算法，所提算法更为有效。

关键词: 结构化学习, 排序算法, 割平面算法, 支持向量机