Open AccessRETRACTED: Research on E-commerce Recommendation System Based on Big Data Technology
Author(s) -
Zhenjie Wan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012159
Subject(s) - spark (programming language) , computer science , recommender system , field (mathematics) , big data , e commerce , architecture , implementation , world wide web , online and offline , data science , database , data mining , software engineering , operating system , art , mathematics , pure mathematics , visual arts , programming language
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.