Clustering the tagged web

12 years 10 months ago
Clustering the tagged web
Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically significant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score inc...
Daniel Ramage, Paul Heymann, Christopher D. Mannin
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where WSDM
Authors Daniel Ramage, Paul Heymann, Christopher D. Manning, Hector Garcia-Molina
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