Paper Info
Title
Scalable Faceted Ranking in Tagging Systems
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 Page Rank-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 You Tube 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.
Year
DOI
Venue
2009
10.1007/978-3-642-12436-5_21
Lecture Notes in Business Information Processing
Keywords
Field
DocType
Web intelligence,Tagging systems,Faceted ranking
Graph,Data mining,World Wide Web,Web intelligence,Information retrieval,Ranking,Computer science,Upload,Power graph analysis,Facet (geometry),Merge (version control),Scalability
Conference
Volume
ISSN
Citations 
45
1865-1348
0
PageRank 
References 
Authors
0.34
15
3
Name
Order
Citations
PageRank
José Ignacio Orlicki121.47
J. Ignacio Alvarez-hamelin214313.31
Pablo Fierens342.14