Deep Multi-view Learning to Rank [article]

Guanqun Cao and Alexandros Iosifidis and Moncef Gabbouj and Vijay Raghavan and Raju Gottumukkala
2018 arXiv   pre-print
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
more » ... ure 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.
arXiv:1801.10402v1 fatcat:esgar3k3fzgthlgd235sfe3wpy