Learning preference relations using Support Vector Regression
JingDong Tan, Rujing Wang · 2011
In this paper we propose a novel approach of learning preference relations using Support Vector Regression (SVR). It answers the problem of consistent ranking and improves the ability of generalization to ranking for the property of SVR method. Meanwhile, the Wilcoxon-Mann-Whitney (WMW) statistic is introduced to evaluate the result of the ranking algorithm. The experiments on an artificial dataset and some benchmark datasets show the effectiveness of the proposed algorithm. An application to ranking in web searching system based on the proposed method is also demonstrated.