Deep Multi-view Learning to Rank

We study the problem of learning to rank from multiple sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We propose a multi-objective solution to ranking by capturing the information of the feature mapping from both within each view as well as across views using autoencoder-like networks. Moreover, a novel end-to-end solution is introduced to enhance the joint ranking with minimum view-specific ranking loss, so that we can achieve the maximum global view agreements within a single optimization process. The proposed method is validated on a wide variety of ranking problems, including university ranking, multi-view lingual text ranking and image data ranking, providing superior results.