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

翻译：在线市场规模每日由个体卖家发起大量价格更新，这种价格民主化带来了数据质量方面的挑战。缺乏传统在线零售商所具备的集中护栏机制，导致网站更容易出现不准确的价格发布，进而造成客户体验下降及潜在收入损失。我们提出MoatPlus（基于树的掩码最优锚点定价、邻近标记与无监督统计特征），这是一种面向增长型市场平台的可扩展价格异常检测框架。其核心思想是通过无监督统计特征的邻近性与历史价格趋势生成价格上限。我们构建了一个集成模型来检测基于价格特征的不规则性，剔除异常特征，并采用优化的加权方案，在实时定价流水线中构建可靠的价格边界。实验表明，我们的方法在高脆弱性商品子集上可将精确锚点覆盖率提升高达46.6%。