Revealing Hidden Connections in Recommendation Networks

Abstract

Companies have been increasingly seeking new mechanisms for making their electronic marketing campaigns to become viral, thus obtaining a cascading recommendation effect similar to word-of-mouth. We analysed a dataset of a magazine publisher that uses email as the main marketing strategy and found out that networks emerging from those campaigns form a very sparse graph. We show that online social networks can be effectively used as a means to expand recommendation networks. Starting from a set of users, called seeders, we crawled Google's Orkut and collected about 20 million users and 80 million relationships. Next, we extended the original recommendation network by adding new edges using Orkut relationships that built a much denser network. Therefore, we advocate that online social networks are much more effective than email-based marketing campaigns