Title: E-commerce assistant application incorporating machine learning image classification

Authors: Victor Chang; Robert Marshall; Qianwen Ariel Xu; Anastasija Nikiforova

Addresses: Artificial Intelligence and Information Systems Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK ' Artificial Intelligence and Information Systems Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK ' Artificial Intelligence and Information Systems Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; IBSS, Xi'an Jiaotong-Liverpool University, Suzhou, China ' Faculty of Computing, University of Latvia, Raiņa Boulevard 19, Riga, LV-1586, Latvia

Abstract: The rise of mobile applications has helped to provide information in a broader network of products remotely. They simplify the identification of products by using their barcode or even an image of the item. This paper, therefore, aims to create an e-commerce assistant Android application that incorporates machine learning, more precisely, image classification, to assist potentially disadvantaged people on a tight budget in looking to save money. This is achieved by collecting an image dataset of essential products, training a machine learning model, and applying it to the developed application. Although the proposed solution appears to be useful and consistent with all the defined goals, the available datasets are currently insufficient, taking into account both their size and quality of individual images, which negatively affect the machine learning model and limit the potential of the solution being developed. We concluded that our final product succeeds in serving the basic functionality of the app's requirements. In the future, we will reach a wider network of users and investigate their needs, then develop these functions into the application.

Keywords: e-commerce applications; e-commerce; electronic commerce; machine learning; image classification; disadvantaged people.

DOI: 10.1504/IJBSR.2023.127711

International Journal of Business and Systems Research, 2023 Vol.17 No.1, pp.1 - 26

Received: 18 Aug 2020
Accepted: 11 Oct 2020
Published online: 15 Dec 2022 *