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dc.contributor.authorAtahan, Pelin
dc.contributor.authorSarkar, S.
dc.date.accessioned2012-08-27T11:29:27Z
dc.date.available2012-08-27T11:29:27Z
dc.date.issued2011-02
dc.identifier.issn0025-1909
dc.identifier.urihttp://hdl.handle.net/10679/254
dc.identifier.urihttp://pubsonline.informs.org/doi/abs/10.1287/mnsc.1100.1266
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractWebsites 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.en_US
dc.language.isoengen_US
dc.publisherInformsen_US
dc.relation.ispartofManagement Science
dc.rightsrestrictedAccess
dc.titleAccelerated learning of user profilesen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0001-5620-3358 & YÖK ID 201043) Atahan, Pelin
dc.contributor.ozuauthorAtahan, Pelin
dc.identifier.volume57
dc.identifier.issue2
dc.identifier.startpage215
dc.identifier.endpage239
dc.identifier.wosWOS:000287355500001
dc.identifier.doi10.1287/mnsc.1100.1266
dc.subject.keywordsPersonalizationen_US
dc.subject.keywordsBayesian learningen_US
dc.subject.keywordsInformation theoryen_US
dc.subject.keywordsRecommendation systemsen_US
dc.identifier.scopusSCOPUS:2-s2.0-79951883958
dc.contributor.authorFemale1


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