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Recommender Systems and their Effects on Consumers: The Fragmentation Debate



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 userÕs taste. 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. 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’ 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. These findings suggest that for this setting, recommender systems are associated with an increase in commonality in consumption and that concerns of fragmentation may be misplaced.

Suggested Citation

  • Daniel Fleder & Kartik Hosanagar & Andreas Buja, 2008. "Recommender Systems and their Effects on Consumers: The Fragmentation Debate," Working Papers 08-44, NET Institute, revised Mar 2010.
  • Handle: RePEc:net:wpaper:0844

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    References listed on IDEAS

    1. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    2. Gal Oestreicher-Singer & Arun Sundararajan, 2009. "Recommendation Networks and the Long Tail of Electronic Commerce," Working Papers 09-03, NET Institute, revised Jan 2009.
    3. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Management Sciences in Research on Personalization," Management Science, INFORMS, vol. 49(10), pages 1344-1362, October.
    4. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    5. Greg Shaffer & Z. John Zhang, 1995. "Competitive Coupon Targeting," Marketing Science, INFORMS, vol. 14(4), pages 395-416.
    6. Marshall Van Alstyne & Erik Brynjolfsson, 2005. "Global Village or Cyber-Balkans? Modeling and Measuring the Integration of Electronic Communities," Management Science, INFORMS, vol. 51(6), pages 851-868, June.
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    recommender systems; collaborative filtering; fragmentation; personalization; long tail;

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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