From rankings to ratings: Rank scoring via active learning

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

From rankings to ratings: Rank scoring via active learning

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

School of Computer Science

Research output: Contribution to journal › Conference article › peer-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 language	English
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 → …

Cite this

@article{99ba66ce39ae4d9ba7e0049ad7096a43,

title = "From rankings to ratings: Rank scoring via active learning",

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.",

author = "Jack O{\textquoteright}Neill and Delany, \{Sarah Jane\} and Namee, \{Brian Mac\}",

note = "Publisher Copyright: {\textcopyright} 2018 CEUR-WS. All rights reserved.; 2nd International Workshop on Augmenting Intelligence with Humans-in-the-Loop, HumL@ISWC 2018 ; Conference date: 09-10-2018",

year = "2018",

language = "English",

volume = "2169",

pages = "38--50",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

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TY - JOUR

T1 - From rankings to ratings

T2 - 2nd International Workshop on Augmenting Intelligence with Humans-in-the-Loop, HumL@ISWC 2018

AU - O’Neill, Jack

AU - Delany, Sarah Jane

AU - Namee, Brian Mac

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85052838490

M3 - Conference article

AN - SCOPUS:85052838490

SN - 1613-0073

VL - 2169

SP - 38

EP - 50

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 9 October 2018

ER -

From rankings to ratings: Rank scoring via active learning

Abstract

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Cite this