Improving Tag-Clouds as Visual Information Retrieval Interfaces

TL;DR

This paper introduces a novel clustering-based method for selecting and arranging tags in tag-cloud interfaces, enhancing visual consistency and reducing semantic density to improve user browsing experience.

Contribution

It proposes a new clustering algorithm for tag selection and layout in tag-clouds, advancing visual information retrieval interfaces.

Findings

Reduces semantic density of tag sets

Improves visual consistency of tag-clouds

Enhances browsing experience

Abstract

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.