Advanced Search
MyIDEAS: Login to save this paper or follow this series

Recommender Systems and their Effects on Consumers: The Fragmentation Debate

Contents:

Author Info

Registered author(s):

    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 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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.netinst.org/Fleder_Hosanagar_08-44.pdf
    Download Restriction: no

    Bibliographic Info

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

    as in new window
    Length: 42 pages
    Date of creation: Dec 2008
    Date of revision: Mar 2010
    Handle: RePEc:net:wpaper:0844

    Contact details of provider:
    Web page: http://www.NETinst.org/

    Related research

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

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. 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.
    2. 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.
    3. 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.
    4. Greg Shaffer & Z. John Zhang, 1995. "Competitive Coupon Targeting," Marketing Science, INFORMS, vol. 14(4), pages 395-416.
    5. 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.
    6. Gal Oestreicher-Singer & Arun Sundararajan, 2009. "Recommendation Networks and the Long Tail of Electronic Commerce," Working Papers 09-03, NET Institute, revised Jan 2009.
    Full references (including those not matched with items on IDEAS)

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:net:wpaper:0844. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nicholas Economides).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.