An Analysis of the Use of Tags in a Blog Recommender System

Abstract

The Web is experiencing an exponential growth in the use of weblogs or blogs , websites containing dated journal-style entries. Blog entries are generally organised using informally defined labels known as tags . Increasingly, tags are being proposed as a 'grassroots' alternative to Semantic Web standards. We demonstrate that tags by themselves are weak at partitioning blog data. We then show how tags may contribute useful, discriminating information. Using content-based clustering, we observe that frequently occurring tags in each cluster are usually good meta-labels for the cluster concept. We then introduce the T r score, a score based on the proportion of high-frequency tags in a cluster, and demonstrate that it is strongly correlated with cluster strength. We demonstrate how the T r score enables the detection and removal of weak clusters. As such, the T r score can be used as an independent means of verifying topic integrity in a cluster-based recommender system.