Qstack: Multi-tag Visual Rankings

Abstract

Multi-tag-based search is quite popular on collaborative websites and sharing-online-content systems. For this kind of search results, the challenge is how to compare grouped-tag values of tag collections on heterogeneous alternatives. This paper introduces a new visualization approach named Qstack for dealing with the challenge. Qstack purpose is to help users to visually rank multi-tags based on grouped-score combination within and across the categorized alternatives. The methodology applying interactive stacked bars, dynamic queries and adaptive focus+context techniques enables users to easily create and adjust grouped-tag rankings of a large number of heterogeneous alternatives. A case study on Flickr photo award allocation will be presented for Qstack demonstration. We conducted a qualitative study for evaluating Qstack effectiveness, and the result indicates that our approach is useful for multi-tag rankings.

Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking

“…Visualization systems have been designed to aid decision-makers in inspecting multiple stakeholders' preferences to reach a consensus decision [2,11,17,20,22,25,33,40,42,47,54,55]. A subset of these tools consider the setting, like ours, in which stakeholder preferences are encoded as rankings [11,23,33].…”