  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
