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This paper investigates learning a ranking function using pairwise constraints in the context of human-machine interaction. As the performance of a learnt ranking model is predominantly determined by the quality and quantity of training data, in this work we explore an active learning to rank approach. Furthermore, since humans may not be able to confidently provide an order for a pair of similar instances we explore two types of pairwise supervision: (i) a set of "strongly" ordered pairs whichdoi:10.1137/1.9781611972832.33 dblp:conf/sdm/DavidsonLQWW13 fatcat:a57qzorzzrajfb7s25utzomcpy