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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, J., Delaney, S.J. & McNamee, B. (2018). From Rankings to Ratings: Rank Scoring Via Active Learning. Emerging Topics in Semantic Technologies. ISWC. doi:10.3233/978-1-61499-894-5-69