A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms

TL;DR

This paper reviews how visualization systems recommend content to users, introduces a four-dimensional design space for such recommendations, and discusses patterns and future research directions in the field.

Contribution

It presents a comprehensive review and a novel four-dimensional design space for surfacing content recommendations in visual analytic platforms.

Findings

Identification of common design patterns in content recommendation

Proposal of a four-dimensional framework for recommendation design

Discussion of future research opportunities in visualization recommendations

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.