Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure

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 folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.