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Evaluating Similarity Measures: A Large-Scale Study in the Orkut Social Network
Ellen Spertus, Mehran Sahami, and Orkut Buyukkokten


Online information services have grown too large for users to navigate without the help of automated tools such as collaborative filtering, which makes recommendations to users based on their collective past behavior. While many similarity measures have been proposed and individually evaluated, they have not been evaluated relative to each other in a large real-world environment. We present an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network. We determine the usefulness of the different recommendations by actually measuring users' propensity to visit and join recommended communities. We also examine how the ordering of recommendations influenced user selection, as well as interesting social issues that arise in recommending communities within a real social network.

Appeared in:
KDD '05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
Chicago, IL, August, 2005.

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©2011 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in KDD '05, August 21-24, 2005 http://doi.acm.org/10.1145/1081870.1081956