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
Learn then Decide: A Learning Approach for Designing Data Marketplaces
Department of Statistics and Data Science, University of California, Los Angeles（加州大学洛杉矶分校统计与数据科学系）；Department of Biostatistics, University of California, Los Angeles（加州大学洛杉矶分校生物统计系）；Department of Biostatistics, University of Pennsylvania（宾夕法尼亚大学生物统计系）
Applications of Signature Methods to Market Anomaly Detection
Department of Mathematics, ETH, Zurich, Switzerland（苏黎世联邦理工学院数学系，瑞士苏黎世）；Department of Banking and Finance, University of Zurich, Zurich, Switzerland（苏黎世大学银行与金融系，瑞士苏黎世）
