Abstract
Nowadays, web collaborative tagging systems which allow users to upload, comment on and recommend contents, are growing. Such systems can be represented as graphs where nodes correspond to users and tagged-links to recommendations. In this paper we analyze the problem of computing a ranking of users with respect to a facet described as a set of tags. A straightforward solution is to compute a PageRank-like algorithm on a facet-related graph, but it is not feasible for online computation. We propose an alternative: (i) a ranking for each tag is computed offline on the basis of tag-related subgraphs; (ii) a faceted order is generated online by merging rankings corresponding to all the tags in the facet. Based on the graph analysis of YouTube and Flickr, we show that step (i) is scalable. We also present efficient algorithms for step (ii), which are evaluated by comparing their results with two gold standards.
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Orlicki, J.I., Alvarez-Hamelin, J.I., Fierens, P.I. (2010). Scalable Faceted Ranking in Tagging Systems. In: Cordeiro, J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2009. Lecture Notes in Business Information Processing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12436-5_21
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DOI: https://doi.org/10.1007/978-3-642-12436-5_21
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