Matching buyers with most suitable sellers providing relevant items (e.g., products) is essential for e-commerce platforms to guarantee customer experience. This matching process is usually achieved through modeling inter-group (buyer-seller) proximity by e-commerce ranking systems. However, current ranking systems often match buyers with sellers of various qualities, and the mismatch is detrimental to not only buyers’ level of satisfaction but also the platforms’ return on investment (ROI). In this paper, we address this problem by incorporating intra-group structural information (e.g., buyer-buyer proximity implied by buyer attributes) into the ranking systems. Specifically, we propose >Deep >Graph >Emb>edding (DEGREE), a deep learning based method, to exploit both inter-group and intra-group proximities jointly for structural learning. With a sparse filtering technique, DEGREE can significantly improve the matching performance with computation resources less than that of alternative deep learning based methods. Experimental results demonstrate that DEGREE outperforms state-of-the-art graph embedding methods on real-world e-commence datasets. In particular, our solution boosts the average unit price in purchases during an online A/B test by up to 11.93%, leading to better operational efficiency and shopping experience.
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机译：使买家与提供相关商品（例如产品）的最合适的卖家相匹配对于电子商务平台来保证客户体验至关重要。通常通过通过电子商务排名系统对组间（买方-卖方）邻近度进行建模来实现此匹配过程。但是，当前的排名系统通常会将买家与各种素质的卖家进行匹配，这种不匹配不仅不利于买家的满意度，也不利于平台的投资回报率（ROI）。在本文中，我们通过将组内结构信息（例如，购买者属性隐含的购买者与购买者的接近度）纳入排名系统来解决此问题。具体来说，我们建议采用基于深度学习的方法> De ep > Gr aph > E mb > e 共同利用群体间和群体内的邻近性进行结构学习。通过稀疏过滤技术，DEGREE可以显着提高匹配性能，并且计算资源少于基于替代深度学习的方法。实验结果表明，DEGREE在现实世界中的电子数据集上优于最新的图形嵌入方法。特别是，我们的解决方案将在线A / B测试期间的购买平均单价提高了11.93％，从而带来了更高的运营效率和购物体验。
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