Recommender systems personalize the browsing and consumption experience to each user's taste in environments with many product choices. Popular applications include product recommendations at e-commerce sites and online newspapers' automated selection of articles to display based on the current reader's interests. This ability to focus more closely on one's taste and filter all else out has spawned criticism that recommenders will fragment consumers. Critics say recommenders cause consumers to have less in common with one another and that the media should do more to increase exposure to a variety of content. We present an empirical study of recommender systems in the music industry. In contrast to concerns that users are becoming more fragmented, we find that in our setting users' purchases become more similar to one another. This increase in purchase similarity occurs for two reasons, which we term volume and taste effects. The volume effect is that consumers simply purchase more after recommendations, increasing the chance of having more purchases in common. The taste effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations. When we view consumers' purchases as a similarity network before versus after recommendations, we find that the network becomes denser and smaller, or characterized by shorter inter-user distances.
Kartik Hosanagar is an Associate Professor at The Wharton School of the University of Pennsylvania. Kartik ´s research work focuses on Internet media and Internet marketing. Kartik has been recognized as one of the world's top 40 business professors under 40. He has received several teaching awards including the MBA and Undergraduate Excellence in Teaching awards at the Wharton School. His research has received several awards, including the William Cooper award for best thesis in Management Science. Kartik is a cofounder of Yodle Inc, a venture-backed firm recently listed among the top 50 fastest growing private firms in the US. Kartik holds a PhD in Management Science and Information Systems from Carnegie Mellon University.
The paper is available for download at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1321962
