Low-quality listings and bad actor behavior in online retail websites
threatens e-commerce business as these result in sub-optimal buying experience
and erode customer trust. When a new listing is created, how to tell it has
good-quality? Is the method effective, fast, and scalable? Previous approaches
often have three limitations/challenges: (1) unable to handle cold start
problems where new sellers/listings lack sufficient selling histories. (2)
inability of scoring hundreds of millions of listings at scale, or compromise
performance for scalability. (3) has space challenges from large-scale graph
with giant e-commerce business size. To overcome these limitations/challenges,
we proposed ColdGuess, an inductive graph-based risk predictor built upon a
heterogeneous seller product graph, which effectively identifies risky
seller/product/listings at scale. ColdGuess tackles the large-scale graph by
consolidated nodes, and addresses the cold start problems using homogeneous
influence1. The evaluation on real data demonstrates that ColdGuess has stable
performance as the number of unknown features increases. It outperforms the
lightgbm2 by up to 34 pcp ROC-AUC in a cold start case when a new seller sells
a new product . The resulting system, ColdGuess, is effective, adaptable to
changing risky seller behavior, and is already in production

本研究提出了基于异构卖家产品图的归纳式图形风险预测器 ColdGuess，能够高效地对规模庞大的产品进行打分以辨别风险，且已在生产环境中得到应用，其在新卖家销售新产品时性能甚至高于 lightgbm 34 个百分点