New Article on Content Recommendation Design to Appear at IEEE VIS and in IEEE TVCG

VACLab-er Zhilan Zhou is first author for a new article that characterizes the design space for content recommendation within visual analytics systems.

VACLab-ers Zhilan Zhou, Wenyuan Wang, Mengtian Guo, Yue Wang, and David Gotz are co-authors of A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms, a new article which will be published in an upcoming issue of IEEE TVCG and presented orally at IEEE VIS in Oklahoma City in October 2022. The paper builds on prior work the team has published on modeling a user’s visual analytic focus and looks at how we can design systems that use that focus to recommend information during a visual analysis.

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