Listwise approach based on the cross‐correntropy for learning to rank

Abstract

The problem of learning to rank is addressed and a novel listwise approach by taking document retrieval as an example is proposed. It first introduces the concept of cross-correntropy into learning to rank and then proposes the listwise loss function based on the cross-correntropy between the ranking list given by the label and the one predicted by training model. The use of the cross-correntropy loss leads to the development of the listwise approach called ListCCE, which employs the gradient descent algorithm to train a neural network model. Experimental results tested on publicly available data sets show that the proposed approach performs better than some existing approaches.