A case study of exploiting data mining techniques for an industrial recommender system
Entity
UAM. Departamento de Ingeniería InformáticaDate
2009Citation
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, 2009Funded by
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.Project
info:eu-repo/grantAgreement/EC/FP6/027122; info:eu-repo/grantAgreement/EC/FP6/033715; info:eu-repo/grantAgreement/EC/FP6/045032Subjects
Recommender Systems; Data Mining; Decision Trees; InformáticaNote
This is an electronic version of the paper presented at the 1st International Workshop on Recommender-based Industrial Applications, held in New York on 2009Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
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.
Files in this item
Google Scholar:Cantador Gutiérrez, Iván
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Elliott, Desmond
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Jose, Joemon M.
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