Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
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.
DOI
DOI:10.3233/978-1-61499-894-5-69
Recommended Citation
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
Publication Details
This work will be published as part of the book "Emerging Topics in Semantic Technologies. ISWC 2018 Satellite Events. E. Demidova, A.J. Zaveri, E. Simperl (Eds.), ISBN: 978-3-89838-736-1, 2018, AKA Verlag Berlin"