Understanding Customer Preferences Using Image Classification – A Case Study

Date
2020-01-07
Authors
Brusch, Ines
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Today, 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.
Description
Keywords
Big Data and Analytics: Pathways to Maturity, convolztional neural network, image classification, suport vector machines, travel
Citation
Extent
11 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 53rd Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.