  Multimedia uploaded content is tagged and recommended by users of
collaborative systems, resulting in informal classifications also known as
folksonomies. Faceted web ranking has been proved a reasonable alternative to a
single ranking which does not take into account a personalized context. In this
paper we analyze the online computation of rankings of users associated to
facets made up of multiple tags. Possible applications are user reputation
evaluation (ego-ranking) and improvement of content quality in case of
retrieval. We propose a solution based on PageRank as centrality measure: (i) a
ranking for each tag is computed offline on the basis of the corresponding
tag-dependent subgraph; (ii) a faceted order is generated by merging rankings
corresponding to all the tags in the facet. The fundamental assumption,
validated by empirical observations, is that step (i) is scalable. We also
present algorithms for part (ii) having time complexity O(k), where k is the
number of tags in the facet, well suited to online computation.
