Tag and topic recommendation systems

Abstract

The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources in order to describe and organize them. A tag cloud provides rough impression of relative importance of each tag within the overall cloud in order to facilitate browsing among numerous tags and resources. The size of its vocabulary may be huge, moreover, it is incomplete and inconsistent. Thus, the goal of our paper is to establish tag and topic recommendation systems. Firstly, for tag recommendation system novel algorithms have been proposed to refine vocabulary, enhance reference counts, and improve font distribution for enriched visualization. Secondly, for topic recommendation system novel algorithms have been provided to construct a special graph from tags and evaluate reference counts for topic identification. The proposed recommendation systems have been validated and verified on the tag cloud of a real-world thesis portal.