IDEAS home Printed from
   My bibliography  Save this article

Accelerated Learning of User Profiles


  • Pelin Atahan

    () (School of Economics and Administrative Sciences, Özye\u{g}in University, Istanbul, Turkey 34662)

  • Sumit Sarkar

    () (School of Management, University of Texas at Dallas, Richardson, Texas 75080)


Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation. This paper was accepted by Sandra Slaughter, information systems.

Suggested Citation

  • Pelin Atahan & Sumit Sarkar, 2011. "Accelerated Learning of User Profiles," Management Science, INFORMS, vol. 57(2), pages 215-239, February.
  • Handle: RePEc:inm:ormnsc:v:57:y:2011:i:2:p:215-239

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Dmitri Kuksov & J. Miguel Villas-Boas, 2010. "When More Alternatives Lead to Less Choice," Marketing Science, INFORMS, vol. 29(3), pages 507-524, 05-06.
    2. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    3. Esther Gal-Or & Mordechai Gal-Or, 2005. "Customized Advertising via a Common Media Distributor," Marketing Science, INFORMS, vol. 24(2), pages 241-253, July.
    Full references (including those not matched with items on IDEAS)


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:57:y:2011:i:2:p:215-239. 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: (Mirko Janc). General contact details of provider: .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.