Research on E-Commerce Personalized Recommendation System based on Big Data Technology

Regarding to the recommendation systems for most e-commerce platforms at this stage, there are some issues such as the single personalized recommendation method and no deeper data analysis. By researching the development background of recommendation systems, domestic and international related research results, recommendation algorithm and its application, and analysis and summary of many e-commerce user behavior data, we propose a personalized recommendation system architecture based on a combination of offline mining, real-time mining, and deep learning technology. After determining the architecture and functions of the system, we started to design the personalized recommendation system in detail. The core idea is to collect and pre-process data in real-time from multiple e-commerce platforms which generate user data and gather all the personalized data of the users to prepare for the next data mining, then use the data mining technology in the big data to recommend personalizing products automatically, so that it can satisfy the personalized requirements of users.