Big Data-Based Recommendation Algorithm in E-commerce Personalized Marketing

Abstract

E-commerce recommendation algorithm is the core of the entire recommendation system, which plays a very important role in e-commerce personalized marketing. Its recommendation accuracy and efficiency directly affect the overall performance of the recommendation system. E-commerce recommendation algorithm based on data mining technology, in-depth analysis of various user data especially user access data, get each user’s hobbies, interests and specific buying behavior characteristics. This paper analyzes the related technologies and algorithms of e-commerce recommendation system, and proposes the architecture of e-commerce recommendation system based on user behavior data. In order to meet the requirements of recommendation accuracy and real-time performance, the recommendation module designed in this paper is mainly composed of three modules: content-based recommendation module, collaborative filtering algorithm-based recommendation module and user behavior-based recommendation module, and the functions and technologies of each part are specifically analyzed. Finally, a personalized marketing scenario is created to evaluate the effect of the recommendation system.