Shen, LeixianShen, EnyaTai, ZhiweiSong, YiranWang, JianminAgus, Marco and Garth, Christoph and Kerren, Andreas2021-06-122021-06-122021978-3-03868-143-4https://doi.org/10.2312/evs.20211061https://diglib.eg.org:443/handle/10.2312/evs20211061General visualization recommendation systems typically make design decisions of the dataset automatically. However, these systems are only able to prune meaningless visualizations but fail to recommend targeted results. In this paper, we contributed TaskVis, a task-oriented visualization recommendation approach with detailed modeling of the user's analysis task. We first summarized a task base with 18 analysis tasks by a survey both in academia and industry. On this basis, we further maintained a rule base, which extends empirical wisdom with our targeted modeling of analysis tasks. Inspired by Draco, we enumerated candidate visualizations through answer set programming. After visualization generation, TaskVis supports four ranking schemes according to the complexity of charts, coverage of the user's interested columns and tasks. In two user studies, we found that TaskVis can well reflect the user's preferences and strike a great balance between automation and the user's intent.Humancentered computingInformation visualizationInformation SystemsData analytics10.2312/evs.2021106191-95