A case study of exploiting data mining techniques for an industrial recommender system

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dc.contributor.author Cantador, Iván
dc.contributor.author Elliott, Desmond
dc.contributor.author Jose, Joemon M.
dc.contributor.other UAM. Departamento de Ingeniería Informática es_ES
dc.date.accessioned 2015-05-08T13:52:58Z
dc.date.available 2015-05-08T13:52:58Z
dc.date.issued 2009
dc.identifier.citation INDREC 2009: Proceedings of the 1st International Workshop on Recommender-based Industrial Applications, at the RecSys 2009: 3rd ACM Conference on Recommender Systems. ACM, New York, October 22-25, 2009 en_US
dc.identifier.uri http://hdl.handle.net/10486/666095
dc.description This is an electronic version of the paper presented at the 1st International Workshop on Recommender-based Industrial Applications, held in New York on 2009 en_US
dc.description.abstract We describe a case study of the exploitation of Data Mining techniques for creating an industrial recommender system. The aim of this system is to recommend items of a fashion retail store chain in Spain, producing leaflets for loyal customers announcing new products that they are likely to want to purchase. Motivated by the fact of having little information about the customers, we propose to relate demographic attributes of the users with content attributes of the items. We hypothesise that the description of users and items in a common content-based feature space facilitates the identification of those products that should be recommended to a particular customer. We present a recommendation framework that builds Decision Trees for the available demographic attributes. Instead of using these trees for classification, we use them to extract those content-based item attributes that are most widespread among the purchases of users who share the demographic attribute values of the active user. We test our recommendation framework on a dataset with oneyear purchase transaction history. Preliminary evaluations show that better item recommendations are obtained when using demographic attributes in a combined way rather than using them independently. en_US
dc.description.sponsorship This research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whole. en_US
dc.format.extent 8 pág. es_ES
dc.format.mimetype application/pdf en
dc.language.iso eng en
dc.subject.other Recommender Systems en_US
dc.subject.other Data Mining en_US
dc.subject.other Decision Trees en_US
dc.title A case study of exploiting data mining techniques for an industrial recommender system en_US
dc.type conferenceObject en
dc.subject.eciencia Informática es_ES
dc.relation.eventdate October 22-25, 2009 en_US
dc.relation.eventnumber 1
dc.relation.eventplace New York (United States) en_US
dc.relation.eventtitle 1st International Workshop on Recommender-based Industrial Applications, INDREC 2009 en_US
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/027122 en
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/033715 en
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/045032 en
dc.type.version info:eu-repo/semantics/publishedVersion en
dc.contributor.group Recuperación de información (ING EPS-008) es_ES
dc.rights.cc Reconocimiento – NoComercial – SinObraDerivada es_ES
dc.rights.accessRights openAccess en
dc.authorUAM Cantador Gutiérrez, Iván (261086)

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