Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure
Giovanni Quattrone, Licia Capra, Pasquale De Meo, Emilio Ferrara,, Domenico Ursino

TL;DR
This paper introduces a new tag similarity measure based on mutual reinforcement to automatically enrich folksonomies, significantly improving resource retrieval accuracy and coverage in social tagging systems.
Contribution
The paper proposes an innovative, fully automatic tag similarity metric that enhances folksonomy density and search effectiveness, addressing limitations of existing methods in sparse tag distributions.
Findings
Higher search accuracy with the new metric
Increased coverage of relevant resources
Effective in sparse folksonomy scenarios
Abstract
Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the…
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