From rankings to ratings: Rank scoring via active learning

Jack O’Neill, Sarah Jane Delany, Brian Mac Namee

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish
Pages (from-to)38-50
Number of pages13
JournalCEUR Workshop Proceedings
Volume2169
Publication statusPublished - 2018
Event2nd International Workshop on Augmenting Intelligence with Humans-in-the-Loop, HumL@ISWC 2018 - Monterey, United States
Duration: 9 Oct 2018 → …

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