Browsing by Author "Schmid, Jenny"

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    A Design Space for Explainable Ranking and Ranking Models
    (The Eurographics Association, 2022) Hazwan, Ibrahim Al; Schmid, Jenny; Sachdeva, Madhav; Bernard, Jürgen; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Item ranking systems support users in multi-criteria decision-making tasks. Users need to trust rankings and ranking algorithms to reflect user preferences nicely while avoiding systematic errors and biases. However, today only few approaches help end users, model developers, and analysts to explain rankings. We report on the study of explanation approaches from the perspectives of recommender systems, explainable AI, and visualization research and propose the first cross-domain design space for explainers of item rankings. In addition, we leverage the descriptive power of the design space to characterize a) existing explainers and b) three main user groups involved in ranking explanation tasks. The generative power of the design space is a means for future designers and developers to create more target-oriented solutions in this only weakly exploited space.
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    RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings
    (The Eurographics Association, 2022) Schmid, Jenny; Cibulski, Lena; Hazwani, Ibrahim Al; Bernard, Jürgen; Bernard, Jürgen; Angelini, Marco
    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.