Recommender systems as cultural intermediaries: Big data and the construction of user identity

RM 215

Recommender systems are a great source of income for online companies. Using big data analytics and users’ history of online transactions, a recommender engine will suggest new products for consumers to consider, thus increasing a company’s business and consumer loyalty. Using a qualitative content analysis on a sample of 127 trade journal articles, we show how recommender systems driven by big data redefine consumer identity. First, we explore the new intimacy created between the consumers and marketers by the use of these recommender systems. Second, we explain the map of identity created through the real-time surveillance of consumer data and through large- scale experiments. Finally, we discuss the role of recommender systems as the new cultural intermediaries that shortcut the trust previously placed in the recommendations of other peers, taste-makers, or cultural critics. We conclude by showing how, with big data-fueled recommender engines, marketers have changed the way individuals experience culture.