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dc.contributor.authorLu, Shashaen
dc.contributor.authorXiao, Len
dc.contributor.authorDing, Men
dc.date.accessioned2016-03-09T13:42:42Z
dc.date.available2016-03-09T13:42:42Z
dc.date.issued2016-05en
dc.identifier.citationMarketing Science 2016en
dc.identifier.issn0732-2399
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/254280
dc.description.abstractIn this paper, we propose an automated and scalable garment recommender system using real time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers’ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper’s behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, non-automated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.
dc.description.sponsorshipThe authors thank the participants in presentations given by the authors in College of Business at City Univeristy of HongKong and Cambridge Judge Business School for their feedback, as well as the Editor, the Area Editor, and two anonymous Marketing Science reviewers for their insightful comments. This research was supported by two National Natural Science Foundation of China Fund (Grants 71232008 & 71502039), and the Institute for Sustainable Innovation and Growth (iSIG) at School of Management, Fudan University.
dc.language.isoenen
dc.publisherInforms
dc.titleA video-based automated recommender (VAR) system for garmentsen
dc.typeArticle
dc.provenanceOA-7519
prism.endingPage510
prism.issueIdentifier3en
prism.publicationDate2016en
prism.publicationNameMarketing Scienceen
prism.startingPage484
prism.volume35en
dcterms.dateAccepted2016-01-27en
rioxxterms.versionofrecord10.1287/mksc.2016.0984en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2016-05en
dc.identifier.eissn1526-548X
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2016-04-25en
rioxxterms.freetoread.startdate2017-04-25


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