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Ranking Model for Domain Specific Search

Learning to rank is an important area at the interface of machine learning, information retrieval and Web search. The technology has been successfully applied to web search, and is becoming one of the key machines for building search engines. The central challenge in optimizing various measures of ranking loss is that the objectives tend to be non-convex and discontinuous. In recent years, boosting, neural networks, support vector machines, and other techniques have been applied. To build a unique ranking model for each domain it time-consuming for training models. In this paper, we address these difficulties by proposing algorithm called ranking adaptation SVM (RA-SVM).Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains.