Impact of recommendations on advertising-based revenue models

Abstract

Online content providers need a loyal user base for achieving a profitable revenue stream. Large number of visits and long clickstreams are essential for business models based on online advertising. In e-commerce settings, personalized recommendations have already been extensively researched on their effect on both user behavior and related economic performance indicators. We transfer this evaluation into the online content realm and show that recommender systems exhibit a positive impact for online content provider as well. Our research hypotheses emphasize on those components of an advertising-based revenue stream, which are manipulable by personalized recommendations. Based on a rich data set from a regional German newspaper the hypotheses are tested and conclusions are derived. © 2012 Springer-Verlag Berlin Heidelberg.