Michael G. Noll

Applied Research. Big Data. Distributed Systems. Open Source.

Telling Experts From Spammers: Expertise Ranking in Folksonomies

My paper Telling Experts from Spammers: Expertise Ranking in Folksonomies, a joint work written together with fellow Ph.D. candidate Ching-Man Au Yeung from University of Southampton, has been accepted for publication and presentation at this year’s International ACM SIGIR Conference which will be held in Boston, USA, from July 19 - 23, 2009.

Update: Head over to the dedicated SPEAR algorithm website for more information!


With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.

Full Paper & Presentation

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