A Marketplace Price Anomaly Detection System at Scale

TL;DR

This paper introduces MoatPlus, a scalable system for detecting price anomalies in online marketplaces by leveraging unsupervised statistical features and ensemble modeling to improve data quality and customer experience.

Contribution

The paper presents a novel scalable framework, MoatPlus, that combines proximity, historical trends, and ensemble models for real-time price anomaly detection in large marketplaces.

Findings

Improved precise anchor coverage by up to 46.6% in high-vulnerability items.

Enhanced data quality and customer experience through real-time anomaly detection.

Effective exclusion of irregular features using ensemble modeling.

Abstract

Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets