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dc.contributor.authorAtahan, Pelin
dc.contributor.authorJohar, M.
dc.contributor.authorSarkar, S.
dc.date.accessioned2016-02-17T11:06:03Z
dc.date.available2016-02-17T11:06:03Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10679/3066
dc.description.abstractPersonalization and recommendation systems are being increasingly utilized by ecommerce firms to provide personalized product offerings to visitors at the firms’ web sites. These systems often recommend, at each interaction, multiple items (referred to as an offer set) that might be of interest to a visitor. When making recommendations firms typically attempt to maximize their expected payoffs from the offer set. This paper examines how a firm can maximize its expected payoffs by leverag ing th e kn owledge of the profiles of visitors to their site. We provide a methodology that accounts for the interactions among items in an offer set in order to determine the expected payoff. Identifying the optimal offer set is a difficult problem when the number of candidate items to rec ommend is large. We develop an efficient heuristic for this problem, and show that it performs well for both small and large problem instances.
dc.language.isoengen_US
dc.publisherSocial Science Research Network
dc.relation.ispartofProccedings of the Nineteenth Annual Workshop on Information Technologies and Systems (WITS)
dc.rightsopenAccess
dc.titleOptimizing offer sets based on user profilesen_US
dc.typeConference paperen_US
dc.description.versionpublisher version
dc.peerreviewedyes
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.startpage7
dc.identifier.endpage12
dc.subject.keywordsPersonalization
dc.subject.keywordsRecommendation
dc.subject.keywordsE-commerce
dc.subject.keywordsProbability theory
dc.identifier.scopusSCOPUS:2-s2.0-84903851926
dc.contributor.authorFemale1
dc.relation.publicationcategoryConference Paper - Institutional Academic Staff


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