Improving Online Customer Shopping Experience with Computer Vision and Machine Learning Methods

Li, Zequn, Li, Honglei and Shao, Ling (2016) Improving Online Customer Shopping Experience with Computer Vision and Machine Learning Methods. In: HCI in Business, Government, and Organizations: eCommerce and Innovation. Springer, London, pp. 427-436. ISBN 978-3-319-39395-7

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Official URL: http://dx.doi.org/10.1007/978-3-319-39396-4_39

Abstract

Computer vision and pattern recognition has achieved great developments in last decade, especially the feature categorizing and detection. How to exploit the new techniques in this research area has rarely discussed in the information systems field. This paper aims at exploring the opportunities from the most recent development from computer vision area from the online shopping experience perspective. We discussed the possibility of extracting meaningful information from images and apply this to the online recommendation system to improve online customer shopping experience. Implications to both researchers and practitioners are discussed. The contribution of these papers are twofold, firstly, we have summarized the state-of-the-art of the computer vision development in the online shopping recommendation system, especially in the fashion industry; secondly, we have provided some potential research gaps for on how computer vision method could be used in the information systems field.

Item Type: Book Section
Uncontrolled Keywords: Online recommendation system, Machine learning, Shopping experience, Image processing, Fashion recommendation
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: Becky Skoyles
Date Deposited: 05 Aug 2016 13:29
Last Modified: 01 Feb 2017 16:04
URI: http://nrl.northumbria.ac.uk/id/eprint/27484

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