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.
@article{arxiv.2208.04219,
title = {A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms},
author = {Zhilan Zhou and Wenyuan Wang and Mengtian Guo and Yue Wang and David Gotz},
journal= {arXiv preprint arXiv:2208.04219},
year = {2023}
}