A Marketplace Price Anomaly Detection System at Scale
This work addresses data quality issues for online marketplace platforms, but it is incremental as it builds on existing anomaly detection methods.
The paper tackles the problem of inaccurate price listings in online marketplaces, which can harm customer experience and revenue, by presenting MoatPlus, a scalable anomaly detection framework that improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets.
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