  We study the problem of learning to rank from multiple information 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 present a generic framework for
multi-view subspace learning to rank (MvSL2R), and two novel solutions are
introduced under the framework. The first solution captures information of
feature mappings from within each view as well as across views using
autoencoder-like networks. Novel feature embedding methods are formulated in
the optimization of multi-view unsupervised and discriminant autoencoders.
Moreover, we introduce an end-to-end solution to learning towards both the
joint ranking objective and the individual rankings. The proposed solution
enhances the joint ranking with minimum view-specific ranking loss, so that it
can achieve the maximum global view agreements in a single optimization
process. The proposed method is evaluated on three different ranking problems,
i.e. university ranking, multi-view lingual text ranking and image data
ranking, providing superior results compared to related methods.
