Abstract:
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.

Bio: Ellen Spertus is a research scientist at Google and an associate professor of computer science at Mills College. She received her bachelor’s, master’s, and doctoral degrees in computer science from MIT. Her research interests include data mining; computer science education, with a focus on gender equity; and online communications and community.