Similar to [16], we employ the Kullback-Leibler divergence to measure the distance between the current model w to be optimized and the previous model w t , and the regularization terms include both the loss suffered at current triplet instance and the confidence on current triplet instance.
we formulate the optimization of second-order online learning to rank as: where γ is the trade-off parameter.