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Reference implementation of SPEAR ranking algorithm released

We have just released the “reference” implementation of our SPEAR ranking algorithm. The library is written in the Python programming language, and should be straight-forward to use. You can install the library via Python’s setuptools/easy_install or download it from GitHub.

Here’s a quick example on how to use it:

>>> import spear
>>> activities = [
... (datetime.datetime(2010,7,1,9,0,0), "alice", ""),
... (datetime.datetime(2010,8,1,12,45,0), "bob", ""),
... ]
>>> spear_algorithm = spear.Spear(activities)
>>> expertise_results, quality_results =

Get the top user and his expertise score:

>>> expertise_score, user = expertise_results[0]
>>> print "%s => %.4f" % (user, expertise_score)
alice => 0.5858

Get the top resource and its quality score:

>>> quality_score, resource = quality_results[0]
>>> print "%s => %.4f" % (resource, quality_score) => 1.0000

You can also use the library to simulate the HITS algorithm of Jon Kleinberg. Simply supply a credit score function C(x) = 1 to the SPEAR algorithm (see the documentation of the method).

Feel free to play around with it and send us feedback.

PS: The SPEAR Python library requires SciPy/NumPy. If you don’t have these installed already, here are some installation instructures to get you started.

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