Abstract
With the continuous development of e-commerce and the increasing number of users, more and more scholars have devoted themselves to the research of recommendation algorithms for e-commerce platforms. How to quickly dig out information or products that users are interested in from massive data is a research category in the field of recommendation systems. The emergence of the Spark memory computing platform can provide technical support for improving the efficiency and real-time performance of the recommendation algorithm. This article first analyzes the requirements of the e-commerce recommendation system, and designs the overall architecture of the system. Through the analysis of the system, a recommendation system that conforms to the e-commerce system is designed, and a stream computing framework is used to implement a recommendation system that can meet offline and online recommendations combined recommendation system.
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