Boosting rank with predictable training error

Listwise approach is an important method to solve practical Web search problem in learning to rank. In this paper, we first analyze the practical Web search problem and construct the model to solve it. Then we propose an algorithm called DiffRank which can apply boosting technology to learning to rank in listwise. Through theoretical analysis, we prove that the upper bound of training error can be reduced in our proposed algorithm. The experimental results further verify our theoretical analysis and demonstrate that our approach can better perform in practical Web search than other state-of-the-art listwise algorithms.