Understanding Customer Preferences Using Image Classification – A Case Study

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dc.contributor.authorBrusch, Ines
dc.date.accessioned2020-01-04T07:20:21Z
dc.date.available2020-01-04T07:20:21Z
dc.date.issued2020-01-07
dc.description.abstractToday, companies have a large amount of data at their disposal. In addition to classic data in text or table form, the number of images also increases enormously. This is particularly the case if the customer contact exists via the Internet, e.g., social networks, blogs or forums. If these images can be evaluated, they lead to a better understanding of the customer. Improved recommendations can be made and customer satisfaction can be increased. This paper shows by means of support vector machines (SVM), convolutional neural networks (CNN) and cluster analyses how it is possible for companies to evaluate image data on their own and thus to understand and classify the customer. The data of travel platform users serve as a case study. Advantages and disadvantages of, as well as prerequisites for SVMs and CNNs are pointed out and segmentation of the users on the basis of their images is made.
dc.format.extent11 pages
dc.identifier.doi10.24251/HICSS.2020.119
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63858
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data and Analytics: Pathways to Maturity
dc.subjectconvolztional neural network
dc.subjectimage classification
dc.subjectsuport vector machines
dc.subjecttravel
dc.titleUnderstanding Customer Preferences Using Image Classification – A Case Study
dc.typeConference Paper
dc.type.dcmiText

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