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Accelerated Learning of User Profiles

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  • 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)

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    Abstract

    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.1100.1266
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 57 (2011)
    Issue (Month): 2 (February)
    Pages: 215-239

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    Handle: RePEc:inm:ormnsc:v:57:y:2011:i:2:p:215-239

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    Related research

    Keywords: personalization; Bayesian learning; information theory; recommendation systems;

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