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In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons.
O'Neill, Jack; Delany, Sarah Jane; and Namee, Brian Mac, "From Rankings to Ratings: Rank Scoring Via Active Learning" (2018). Conference papers. 265.