Novel Ranking Algorithm Based on Listwise
LI Long-shu · Jisuanji gongcheng · 2011
The discriminant model learned by traditional ranking algorithm based on Pairwise method is not accurate.Aiming at the above problem,a novel ranking algorithm based on Listwise method is proposed.The algorithm uses the discriminative model and adopts one-slack Support Vector Machine(SVM) as its framework.On this basis,the algorithm defines its objective function.In order to solve the problem of exponential size of constraints in the optimization,a cutting plane algorithm is introduced.For the sub-problem of finding the most violated constraints,the paper presents to view it as a linear assignment problem,and adopts Hungarian method to solve it.Experimental results on the benchmark datasets prove the effectiveness and stability of the algorithm.