论文标题
Coldguess：一个通用有效的关系图卷积网络，以解决冷启动案例
ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases
论文作者
论文摘要
在线零售网站中的低质量列表和不良演员行为威胁到电子商务业务，因为这会导致优化的购买经验并削弱客户信任。当创建新的清单时，如何分辨它具有良好的质量？该方法有效，快速且可扩展吗？以前的方法通常有三个限制/挑战：（1）无法处理新卖家/列表缺乏足够销售历史的冷门问题。 （2）无法大规模评分数亿个清单，或损害性能以进行可伸缩性。 （3）从具有巨大的电子商务业务规模的大型图表中面临空间挑战。为了克服这些局限性/挑战，我们提出了Coldguess，这是基于归纳图的风险预测指标，建立在异质卖方产品图上，该图表有效地确定了危险的卖方/产品/列表。 Coldguess通过合并节点来处理大规模图，并通过均匀影响解决了冷启动问题1。对实际数据的评估表明，随着未知功能的数量增加，Coldguess具有稳定的性能。当新卖家出售新产品时，在冷启动案例中，它的表现最高为34个PCP ROC-AUC。最终的系统Coldguess是有效的，可适应不断变化的风险卖方行为，并且已经在生产中
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
