Ranking in information streams

Abstract

Information streams allow social network users to receive and interact with the latest messages from friends and followers. But as our social graphs grow and mature it becomes increasingly difficult to deal with the information overload that these realtime streams introduce. Some social networks, like Facebook, use proprietary interestingness metrics to rank messages in an effort to improve stream relevance and drive engagement. In this paper we evaluate learning to rank approaches to rank content based on a variety of features taken from live-user data.