Listwise learning to rank with extreme order sensitive constraint via cross‐correntropy

Abstract

SummaryIn information retrieval, learning to rank was originally proposed for ranking retrieved documents according to their relevance by machine learning techniques. In general, it can effectively improve the performance of critical ranking tasks, where the listwise approach is one of the most popular approaches adopted for ranking. However, considering that documents with more relevance receive greater attention, current ranking function based listwise approaches discriminate correctly ranked sequences with incorrect sequences; however, they are incapable of recognizing which incorrect sequences are relatively more satisfied. In this study, we propose a listwise approach based on an extreme order sensitive constraint to overcome the aforementioned drawback. We first define this constraint and then design a novel listwise loss function, ListXOS, based on the constraint via cross‐correntropy to improve the performance of ranking tasks. Experimental results on three public datasets present improved performance of learning to rank by 6% compared with conventional methods, which demonstrate the superiority of the proposed approach over related state‐of‐the‐art approaches.