Publikationen von Jan-Frederik Kassel

2019

  • Online Learning of Visualization Preferences through Dueling Bandits for Enhancing Visualization Recommendations
    Jan-Frederik Kassel and Michael Rohs
    EuroVis 2019 - Short Papers
    A visualization recommender supports the user through automatic visualization generation. While previous contributions primarily concentrated on integrating visualization design knowledge either explicitly or implicitly, they mostly do not consider the user's individual preferences. In order to close this gap we explore online learning of visualization preferences through dueling bandits. Additionally, we consider this challenge from a usability perspective. Through a user study (N = 15), we empirically evaluate not only the bandit's performance in terms of both effectively learning preferences and properly predicting visualizations (satisfaction regarding the last prediction: μ = 85%), but also the participants' effort with respect to the learning procedure (e.g., NASA-TLX = 24:26). While our findings affirm the applicability of dueling bandits, they further provide insights on both the needed training time in order to achieve a usability-aligned procedure and the generalizability of the learned preferences. Finally, we point out a potential integration into a recommender system.
  • Talk to Me Intelligibly: Investigating An Answer Space to Match the User's Language in Visual Analysis
    Jan-Frederik Kassel and Michael Rohs
    Proceedings of the 2019 on Designing Interactive Systems Conference
    Conversational interfaces (CIs) have the potential to empower a broader spectrum of users to independently conduct visual analysis. Yet, recent approaches do not fully consider the user's characteristics. In particular, the objective of matching the user's language has been understudied in visual analysis. In order to close this gap, we introduce an answer space motivated by Grice's cooperative principle for framing personalized communication in complex data situations. We conducted both an online survey (N=76) to analyze communication preferences and a qualitative experiment (N=10) to investigate personalized conversations with an existing CI. In order to match the user's language properly, our results suggest to consider additional user characteristics along with their knowledge level. While mismatching communication preferences triggers negative reactions, a preference-aligned communication evokes positive reactions. As our analysis confirms the importance of matching the user's language in visual analysis, we provide design implications for future CIs.

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