Deep Multi-view Learning to Rank

--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. Learning to rank is an important research topic in information retrieval and data mining, which aims to learn a ranking model to produce a query-specfic ranking list. The ranking model establishes a relationship between each pair of data samples by combining the corresponding features in an optimal way [1]. A score is then assigned to each pair to evaluate its relevance forming a global ranking list across all pairs. The success of learning to rank solutions has brought a wide spectrum of applications, including online advertising [2], natural language processing [3] and multimedia retrieval [4]. Learning appropriate data representation and a suitable scoring function are two vital steps in the ranking problem. T raditionally, a feature mapping models the data distribution in a latent space to match the relevance relationship, while the scoring function is used to quantify the relevance measure [1]; however, the ranking problem in the real world emerges from multiple facets and data patterns are mined from diverse domains.