Faceted Ranking in Collaborative Tagging Systems - Efficient Algorithms for Ranking Users based on a Set of Tags

Multimedia content is uploaded, tagged and recommended by users of collaborative systems such as YouTube and Flickr. These systems can be represented as tagged-graphs, where nodes correspond to users and taggedlinks to recommendations. In this paper we analyze the online computation of user-rankings associated to a set of tags, called a facet. A simple approach to faceted ranking is to apply an algorithm that calculates a measure of node centrality, say, PageRank, to a subgraph associated with the given facet. This solution, however, is not feasible for online computation. We propose an alternative solution: (i) first, a ranking for each tag is computed offline on the basis of tag-related subgraphs; (ii) then, a faceted order is generated online by merging rankings corresponding to all the tags in the facet. Based on empirical observations, we show that step (i) is scalable. We also present efficient algorithms for step (ii), which are evaluated by comparing their results to those produced by the direct calculation of node centrality based on the facet-dependent graph.