Qstack: Multi-tag Visual Rankings
Phi Giang Pham, Mao Lin Huang · Journal of Software · 2016
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.