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Recommender Systems and their Effects on Consumers: The Fragmentation Debate

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Author Info
Daniel Fleder () (Department of Operations and Information Management, The Wharton School)
Kartik Hosanagar () (Department of Operations and Information Management, The Wharton School)
Andreas Buja () (Department of Statistics, The Wharton School)
Abstract

Recommender systems are becoming integral to how consumers discover media. The value that recommenders offer is personalization: in environments with many product choices, recommenders personalize the browsing and consumption experience to each user’s taste. Popular applications include product recommendations at e-commerce sites and online newspapers’ selecting 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. Others, however, contend that recommenders do the opposite: they may homogenize users because they share information among those who would otherwise not communicate. These are opposing views, discussed in the literature for over ten years for which there is not yet empirical evidence. 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 become more similar to one another in their purchases. This increase in 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 as a similarity network before versus after recommendations, we find that the network becomes denser and smaller, or characterized by shorter inter-user distances. These findings suggest that for this setting, recommender systems are associated with an increase in commonality among users and that concerns of fragmentation may be misplaced.

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Publisher Info
Paper provided by NET Institute in its series Working Papers with number 08-44.

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Length: 40 pages
Date of creation: Dec 2008
Date of revision: Sep 2009
Handle: RePEc:net:wpaper:0844

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Web page: http://www.NETinst.org/

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Related research
Keywords: recommender systems; collaborative filtering; fragmentation; personalization; long tail;

Find related papers by JEL classification:
O3 - Economic Development, Technological Change, and Growth - - Technological Change

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This page was last updated on 2009-12-13.


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