  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.
