Title: Deep Multi-view Learning to Rank,
Abstract: 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.