Cross-border E-commerce Logistics Development Model Based on Data Mining and QoS

The development of cross-border e-commerce has become an important endogenous driving force for China's consumption-driven economic growth. Cross-border e-commerce refers to an international trade activity in which transaction subjects in different countries electronically conclude transactions, make payment and settlement through traditional trade links through e-business platforms and means, and deliver goods and complete transactions through cross-border logistics and long-distance warehousing. Big data mining engine is also limited by its professional use mode, resulting in a skill threshold. At the same time, the popularity of mobile terminals makes people's working environment no longer fixed. In the case that the cluster is often shared by multiple users, a single task often occupies a large amount of computing resources, and the mining of the same data by multiple tasks will inevitably produce repeated operations, which will bring about problems such as low scheduling flexibility, idle resources and low processing efficiency. QoS (Quality of Service) is an important nonfunctional parameter that affects user experience. However, QoS parameter values are generally not easy to obtain, so the typical Web service recommendation system has the problem of sparse QoS data, which reduces the accuracy of prediction. In order to solve this problem, this paper proposes a recommendation method based on user preference to predict the value of QoS to recommend better quality service.