Multi-partite ranking with multi-class AdaBoost algorithm

The algorithms on learning to rank can traditionally be categorized as three classes including point-wise, pair-wise and list-wise. In our work, we focus on the regression-based method for the multi-partite ranking problems due to the efficiency of the point-wise methods. We proposed two ranking algorithms with the real AdaBoost and the discrete AdaBoost, which compute the expectation of the ratings with the estimation of the pseudo posterior probabilities. We found that it can be explained in the framework of the regression with the squared loss. It is more easily implemented than the previous McRank method since the algorithm adopts the decision stump as the weak leaner instead of the regression tree. In the fifteen benchmark datasets, our methods achieve better performance than the pair-wise method RankBoost under the C-index, NDCG and variant of NDCG measures. It has the lower training time complexity than RankBoost but the identical test time complexity.