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Ranking problems, also known as preference learning problems, define a widelyspread class of statistical learning problems with many applications, includingfraud detection, document ranking, medicine, credit risk screening, imageranking or media memorability. In this article, we systematically reviewdifferent types of instance ranking problems, i.e., ranking problems thatrequire the prediction of an order of the response variables, and thecorresponding loss functions resp. goodness criteria. We discuss thedifficulties when trying to optimize those criteria. As for a detailed andcomprehensive overview of existing machine learning techniques to solve suchranking problems, we systemize existing techniques and recapitulate thecorresponding optimization problems in a unified notation. We also discuss towhich of the ranking problems the respective algorithms are tailored andidentify their strengths and limitations. Computational aspects and openresearch problems are also considered.

A review on ranking problems in statistical learning