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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 thearXiv:1801.10402v1 fatcat:esgar3k3fzgthlgd235sfe3wpy