Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector (2008) Social Tag Prediction. In: 31st Annual International ACM SIGIR Conference (SIGIR'08), 20-24 July 2008, Singapore.
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In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Social Bookmarking, Collaborative Tagging Systems, Tag Prediction, Tag Recommendation, del.icio.us|
|Subjects:||Computer Science > Data Mining|
Computer Science > Databases and the Web
Computer Science > Digital Libraries
Computer Science > Semistructured Data
|Related URLs:||Author Homepage||http://heymann.stanford.edu/|
|Deposited By:||Import Account|
|Deposited On:||16 Jun 2008 17:00|
|Last Modified:||21 Nov 2008 14:08|
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