Enhancing Product Safety in E-Commerce with NLP

TL;DR

This paper presents a novel NLP-based system for identifying potentially unsafe products in e-commerce by classifying customer claims, addressing challenges of noisy, multilingual data, and demonstrating deployment in a real-world platform.

Contribution

It introduces an NLP approach for safety issue detection in e-commerce, handling imbalanced multilingual data, and details the deployment process on a large platform.

Findings

Effective classification of safety issues from customer claims.

Robust performance despite noisy, multilingual data.

Successful deployment in a real-world e-commerce setting.

Abstract

Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform. Despite stringent quality and safety checking of products listed on these platforms, occasionally customers might receive a product that can pose a safety issue arising out of its use. In this paper, we present an innovative mechanism of how a large scale multinational e-commerce platform, Zalando, uses Natural Language Processing techniques to assist timely investigation of the potentially unsafe products mined directly from customer written claims in unstructured plain text. We systematically describe the types of safety issues that concern Zalando customers. We demonstrate how we map this core business problem into a supervised text classification problem with highly imbalanced, noisy, multilingual data in a AI-in-the-loop setup with a focus on Key Performance Indicator (KPI) driven evaluation. Finally, we present detailed ablation studies to show a comprehensive comparison between different classification techniques. We conclude the work with how this NLP model was deployed.