Abstract
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.
Original language | English |
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Pages (from-to) | 38-50 |
Number of pages | 13 |
Journal | CEUR Workshop Proceedings |
Volume | 2169 |
Publication status | Published - 2018 |
Event | 2nd International Workshop on Augmenting Intelligence with Humans-in-the-Loop, HumL@ISWC 2018 - Monterey, United States Duration: 9 Oct 2018 → … |