Query dependent learning to model based on ordered multiple hyperplanes

This paper proposes a ranking model that trains different hyperplanes for different queries and optimizes hyperplanes with the order relations. It aims at solving the problem of most existing rank methods that do not consider the significant differences between queries and only resort to a single function that is time consuming. Next, a weighted voting method is proposed to aggregate the ranking lists of the hyperplanes as the final rank. The weights reflect the degree of precision. Effectiveness is tested by the benchmark data set LETOR OHSUMED and is compare with other ranking models. The proposed method shows improved ranking performance with a significant reduction of training time.