A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms

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Zhilan Zhou, Wenyuan Wang, Mengtian Guo, Yue Wang, David Gotz

 View presentation:2022-10-19T14:36:00ZGMT-0600Change your timezone on the schedule page
2022-10-19T14:36:00Z

Exemplar figure, described by caption below — A hypothetical visual analytics platform with an interface includes two tabs: one with visualizations and another with a text-based list. A pop-up window is on top of the interface with content recommendations.

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Abstract

Recommendation algorithms have been leveraged in various ways within visualization systems to assist users as they perform of a range of information tasks. One common focus for these techniques has been the recommendation of content, rather than visual form, as a means to assist users in the identification of information that is relevant to their task context. A wide variety of techniques have been proposed to address this general problem, with a range of design choices in how these solutions surface relevant information to users. This paper reviews the state-of-the-art in how visualization systems surface recommended content to users during users' visual analysis; introduces a four-dimensional design space for visual content recommendation based on a characterization of prior work; and discusses key observations regarding common patterns and future research opportunities.