Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional Networks

Many e-commerce platforms, such as AliExpress, run major promotion campaigns regularly. Before such a promotion, it is important to predict potential best sellers and their respective sales volumes so that the platform can arrange their supply chains and logistics accordingly. For items with a sufficiently long sales history, accurate sales forecast can be achieved through the traditional statistical forecasting techniques. Accurately predicting the sales volume of a new item, however, is rather challenging with existing methods; time series models tend to overfit due to the very limited historical sales records of the new item, whereas models that do not utilize historical information often fail to make accurate predictions, due to the lack of strong indicators of sales volume among the item's basic attributes. This article presents the solution deployed at Alibaba in 2019, which had been used in production to prepare for its annual "Double 11" promotion event whose total sales amount exceeded U.S. $ 38 billion in a single day. The main idea of the proposed solution is to predict the sales volume of each new item through its connections with older products with sufficiently long sales history. In other words, our solution considers the cross-selling effects between different products, which has been largely neglected in previous methods. Specifically, the proposed solution first constructs an item graph, in which each new item is connected to relevant older items. Then, a novel multitask graph convolutional neural network (GCN) is trained by a multiobjective optimization-based gradient surgery technique to predict the expected sales volumes of new items. The designs of both the item graph and the GCN exploit the fact that we only need to perform accurate sales forecasts for potential best-selling items in a major promotion, which helps reduce computational overhead. Extensive experiments on both proprietary AliExpress data and a public dataset demonstrate that the proposed solution achieves consistent performance gains compared to existing methods for sales forecast.