The purpose of this implementation is to make the inner workings of
the algorithm easy to understand and not to distract or confuse
the reader with highly optimized code.
The SPEAR algorithm takes a list of user activities on resources
as input, and returns ranked lists of users by expertise scores
and resources by quality scores, respectively.
You can also use this library to simulate the HITS algorithm of
Jon Kleinberg. Simply supply a credit score function C(x) = 1 to
the SPEAR algorithm (see documentation of Spear.run()).
More information about the SPEAR algorithm is available at:
* "Telling Experts from Spammers: Expertise Ranking in Folksonomies"
Michael G. Noll, Ching-man Au Yeung, et al.
SIGIR 09: Proceedings of 32nd International ACM SIGIR Conference
on Research and Development in Information Retrieval, Boston, USA,
July 2009, pp. 612-619, ISBN 978-1-60558-483-6
The code is licensed to you under version 2 of the GNU General Public
Copyright 2009-2010 Michael G. Noll <http: www.michael-noll.com=""/>
Ching-man Au Yeung <http: www.albertauyeung.com=""/>
TODO: Brief introduction on what you do with files - including link to relevant help section.