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