A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms
arXiv:2208.04219 · doi:10.1109/TVCG.2022.3209445
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.
arXiv commentAccepted to VIS2022
References in corpus (4)
- The State of the Art in Integrating Machine Learning into Visual Analytics
- Gaze-Driven Adaptive Interventions for Magazine-Style Narrative Visualizations
- Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis
- Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis