In this paper, we describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, information that is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.

翻译：在本文中,我们描述了应对电子商务中一系列共同挑战的解决办法,这些挑战来自新产品不断被添加到目录中的事实,挑战涉及使客户经验具有适当的个性,预测需求并规划产品范围,我们争辩说,解决所有这些问题的基本内容是每一份产品有一致和详细的信息,鉴于供应商众多,产品种类众多,这些信息很少获得或一致。我们详细描述了ASOS(世界上最大的电子商务时装零售商之一)为解决这一问题而实施的架构和方法。然后我们展示如何利用对产品的定量理解来改进混合推荐系统方法的建议。