Recommending Twitter users to follow using content and collaborative filtering approaches

Abstract

Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the ex-emplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem - for a given user, UT which other users might be recommended as follow-ers/followees - but unlike other researchers we attempt to harness the real-time web as the basis for profiling and rec-ommendation. To this end we evaluate a range of difierent profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and eficient followee recommen-dation. Copyright 2010 ACM.