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

Recommendation Networks and the Long Tail of Electronic Commerce

Contents:

Author Info

Registered author(s):

    Abstract

    It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or "blockbusters" to less popular or "niche" products, and that electronic markets will therefore be characterized by a "long tail" of demand and revenue. In this paper, we develop a novel method to test this conjecture and we report on results contrasting the demand distributions of books in over 200 distinct categories on Amazon.com. Viewing each product as having a unique position in a hyperlinked network of recommendations between product that is analogous to shelf position in traditional commerce, we quantify the extent to which a product is influenced by its recommendation network position by using a variant of Google’s PageRank measure of centrality. We then associate the average level of network influence on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coefficients derived from the category’s Lorenz curve. We establish that categories whose products are influenced more by recommendations have significantly flatter demand distributions, even after controlling for variations in average category demand, the category’s size and measures of price dispersion. Our empirical findings indicate that doubling the average influence of recommendations on a category is associated with an average increase in the relative demand for the least popular 20% of products by about 50%, and a average reduction in the relative demand for the most popular 20% by about 12%. We also show that this effect is enhanced when there is assortative mixing in the recommendation network, and in categories whose products are more evenly influenced by recommendations. The direction of these results persist across time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work offers new ideas for assessing the influence of networks on demand and revenue patterns in electronic commerce, and provides new empirical evidence supporting the impact of visible recommendations on the long tail of electronic commerce.

    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/Oestreicher-Singer_Sundararajan_09-03.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 09-03.

    as in new window
    Length: 36 pages
    Date of creation: Jan 2009
    Date of revision: Jan 2009
    Handle: RePEc:net:wpaper:0903

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

    Related research

    Keywords: networks; social networks; electronic commerce; ecommerce; recommender systems; influence; gini coefficient;

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. 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.

    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:0903. 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.