论文标题
在视觉分析平台中浮出内容建议的设计空间
A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms
论文作者
论文摘要
推荐算法已在可视化系统中以各种方式利用，以帮助用户执行一系列信息任务。这些技术的一个普遍重点是内容的建议，而不是视觉形式，是帮助用户识别与其任务上下文相关的信息的一种手段。已经提出了各种各样的技术来解决这一普遍问题，并在这些解决方案如何向用户呈现相关信息方面具有一系列设计选择。本文回顾了可视化系统如何在用户视觉分析期间向用户推荐的内容的最新内容；基于先前工作的表征，引入了一个四维设计空间，用于视觉内容推荐；并讨论有关常见模式和未来研究机会的关键观察。
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.
