¹University of Technology, Sydney, New South Wales, Australia.
²Tianjin University, Tianjin, China.

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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.

Index Terms—Multiple tags, visual rankings, interactive stacked bars.

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Cite: Phi Giang Pham, Mao Lin Huang, "Qstack: Multi-tag Visual Rankings," Journal of Software vol. 11, no. 7, pp. 695-703, 2016.