Deep Multi-view Learning to Rank

Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj, Vijay Raghavan, Raju Gottumukkala

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
Pages (from-to)1426-1438
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number4
Publication statusE-pub ahead of print - 20 Sep 2019
Publication typeA1 Journal article-refereed


  • Correlation
  • Transforms
  • Neural networks
  • Training
  • Optimization
  • Data models
  • Data mining
  • Learning to rank
  • multi-view data analysis
  • ranking

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