Our Contribution: Finding Communities What are the features of human communities?

1. Why do we care about recommendations? • NetFlix Prize • Amazon.com “Other users liked” • Helps people discover new products • We focus on recommending groups in social networks to users. There can be millions of groups to choose from. • Our NewKid Algorithm • Your interests influence who your friends you are • Exploit the research your communities have put into finding appropriate groups and recommend those • Algorithm idea: Given the social network, a list of communities and a new user with some friendships: • 1) For each group and each community, the probability that the community will recommend the group is the fraction of users that are members of the group. • 2) The probability that the communities recommendations apply to this user is the fraction of the community that friends the user. • The score for each group is the product of these two values summed over all communities. Recommend the groups that have the maximum score. Helping the New Kid on the Block:Recommending Groups in Social NetworksIsabelle Stanton, istanton@cs.virginia.edu a community • Experiments • Two crawls: LiveJournal (5.5 million users) and Orkut (3.8 million users) • Held out 10% of the graph and found the communities in the rest of the graph. • Ran the NewKid algorithm for each held out vertex • Compared our recommendations with their stated group memberships • We compare our results to an algorithm that recommends groups in order of their total size in the network. • Our Contribution: Finding Communities • What are the features of human communities? • A set of people who know more people in the community than people outside the community do. • Communities overlap; people belong to many communities at once. • Some people are loners and belong to no communities • Previous graph clustering criteria don’t capture these ideas well so we develop a new graph clustering criterion that does. Furthermore, we created 3 algorithms to find these new clusters. Results Our algorithm performs significantly better. # recommendations needed to satisfy 50% of users Orkut Results LiveJournal Results Future Work We recommended groups using only the structure of the graphs. If we included textual meta data, like stated purpose of the group and the user’s interests, we could only improve our recommendations. This would extend the work from collaborative filtering to incorporating content filtering.