Suggesting Friends Using the Implicit Social Graph
Venue
Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2010)
Publication Year
2010
Authors
Maayan Roth, Assaf Ben-David, David Deutscher, Guy Flysher, Ilan Horn, Ari Leichtberg, Naty Leiser, Yossi Matias, Ron Merom
BibTeX
Abstract
Although users of online communication tools rarely categorize their contacts into
groups such as "family", "co-workers", or "jogging buddies", they nonetheless
implicitly cluster contacts, by virtue of their interactions with them, forming
implicit groups. In this paper, we describe the implicit social graph which is
formed by users' interactions with contacts and groups of contacts, and which is
distinct from explicit social graphs in which users explicitly add other
individuals as their "friends". We introduce an interaction-based metric for
estimating a user's affinity to his contacts and groups. We then describe a novel
friend suggestion algorithm that uses a user's implicit social graph to generate a
friend group, given a small seed set of contacts which the user has already labeled
as friends. We show experimental results that demonstrate the importance of both
implicit group relationships and interaction-based affinity ranking in suggesting
friends. Finally, we discuss two applications of the Friend Suggest algorithm that
have been released as Gmail Labs features.