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  4. RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings
 
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2022
Conference Paper
Title

RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings

Abstract
Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools either do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, or lack flexibility in expressing various types of user preferences. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. RankASco leverages a series of visual interfaces, enabling broad user groups to a) select attributes of interest, b) express preferences on attribute scorings based on different mental models, and c) analyze and refine item ranking results.
Author(s)
Schmid, Jenny
Univ. Zürich, Institut für Informatik
Cibulski, Lena  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Hazwani, Ibrahim al
Univ. Zürich, Institut für Informatik
Bernard, Jürgen
Univ. Zürich, Institut für Informatik
Mainwork
EuroVA 2022, 13th International EuroVis Workshop on Visual Analytics  
Conference
International Workshop on Visual Analytics 2022  
DOI
10.2312/eurova.20221072
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Digitized Work

  • Research Line: Human computer interaction (HCI)

  • Research Line: Modeling (MOD)

  • Visual analytics

  • Rankings

  • Decision making

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