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

Faceted Ranking of Egos in Collaborative Tagging Systems