Semi-automatic identification of counterfeit offers in online shopping platforms

TL;DR

This paper presents a semi-automatic workflow to identify likely counterfeit offers in online shopping platforms, aiming to assist experts in manual verification and reduce effort in combating product counterfeiting.

Contribution

It introduces a novel semi-automatic method combining query generation, clustering, and suspiciousness assessment for counterfeit detection in e-commerce.

Findings

Preliminary evaluation on eBay demonstrates the approach's potential.

Workflow effectively clusters similar offers for easier review.

Supports scalable identification of counterfeit offers with limited manual effort.

Abstract

Product counterfeiting is a serious problem causing the industry estimated losses of billions of dollars every year. With the increasing spread of e-commerce, the number of counterfeit products sold online increased substantially. We propose the adoption of a semi-automatic workflow to identify likely counterfeit offers in online platforms and to present these offers to a domain expert for manual verification. The workflow includes steps to generate search queries for relevant product offers, to match and cluster similar product offers, and to assess the counterfeit suspiciousness based on different criteria. The goal is to support the periodic identification of many counterfeit offers with a limited amount of manual effort. We explain how the proposed approach can be realized. We also present a preliminary evaluation of its most important steps on a case study using the eBay platform.