RankBooster: Visual analysis of ranking predictions

• Main Authors: PURI, Abishek, KU, Bon Kyung, WANG, Yong, QU, Huamin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Human-centered computing; Visual analytics; Information visualization; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5358; https://ink.library.smu.edu.sg/context/sis_research/article/6362/viewcontent/2004.06435.pdf
• Institution: Singapore Management University

Ranking is a natural and ubiquitous way to facilitate decision-making in various applications. However, different rankings are often used for the same set of entities, with each ranking method placing emphasis on different factors. These factors can also be multi-dimensional in nature, compounding the problem. This complexity can make it challenging for an entity which is being ranked to understand what they can do to improve their rankings, and to analyze the effect of changes in various factors to their overall rank. In this paper, we present RankBooster, a novel visual analytics system to help users conveniently investigate ranking predictions. We take university rankings as an example and focus on helping universities to better explore their rankings, where they can compare themselves to their rivals in key areas as well as overall. Novel visualizations are proposed to enable efficient analysis of rankings, including a Scenario Analysis View to show a high-level summary of different ranking scenarios, a Relationship View to visualize the influence of each attribute on different indicators and a Rival View to compare the ranking of a university and those of its rivals. A case study demonstrates the usefulness and effectiveness of RankBooster in facilitating the visual analysis of ranking predictions and helping users better understand their current situation