Handling consumer vulnerability in e-commerce product images using machine learning

Sarvjeet Kaur Chatrath, G.S Batra, Yogesh Chaba

Research output: Contribution to journalArticlepeer-review

Abstract

Need
In recent years, secondhand products have received widespread attention, which has raised interest in them. The susceptibility issues that consumers encounter while buying online products in reference to the display images of the products are also not well researched.

Motivation
Retailers employ clever tactics such as ratings, product reviews, etc., to establish a strong position thereby boosting their sales and profits which may have an indirect impact on the consumer purchase that was not aware of that retailer's behavior. This has led to the novel method that has been suggested in this work to address these issues.

Proposed methodology
In this study, a handling method for reused product images based on user vulnerability in e-commerce websites has been developed. This method is called product image-based vulnerability detection (PIVD). The convolutional neural network is employed in three steps to identify the fraudulent dealer, enabling buyers to purchase goods with greater assurance and fewer damages.

Summary
This work is suggested to boost consumers' confidence in order to address the issues they encounter when buying secondhand goods. Both image processing and machine learning approaches are utilized to find vulnerabilities. On evaluation, the proposed method attains an F1 score of 2.3% higher than CNN for different filter sizes, 4% higher than CNN-LSTM when the learning rate is set to 0.008, and 6% higher than CNN when dropout is 0.5.
Original languageEnglish
Article numbere10743
Pages (from-to)1-13
Number of pages13
JournalHeliyon
Volume8
DOIs
Publication statusE-pub ahead of print - 24 Sep 2022

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