Design and Development of E-commerce Recommendation System Based on Big Data Technology

DOI

10.2991/978-94-6463-210-1_6How to use a DOI?

Keywords

big data technology; electronic commerce; recommendation algorithm; Hadoop; computer application

Abstract

E-commerce recommendation system is the key to solve the problem of information explosion and information overload faced by consumers in the process of online shopping, and it is also an important means to tap the potential needs of consumers. Faced with the shortcomings of the current e-commerce platform recommendation system, such as low accuracy, single recommendation scheme and lack of in-depth analysis, this paper will focus on the recommendation algorithm, and integrate the conventional Item-CF and Use-rCF algorithms with the help of K-means clustering algorithm to improve the adaptability of collaborative filtering recommendation algorithm. In addition, a distributed data processing server will be built with the help of Hadoop framework to collect and store massive data, and the recommended algorithm will run smoothly with the help of Spark distributed computing engine. The overall deployment of the recommendation system will be between the user I/O interface and the e-commerce platform, subject to the call and control of the e-commerce platform Web Server. The test results show that the system has improved the recommendation efficiency and accuracy to some extent, and made a useful attempt to promote the intelligent development of e-commerce recommendation service.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Jing Gao
PY  - 2023
DA  - 2023/07/25
TI  - Design and Development of E-commerce Recommendation System Based on Big Data Technology
BT  - 2023 4th International Conference on E-Commerce and Internet Technology (ECIT 2023)
PB  - Atlantis Press
SP  - 36
EP  - 40
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-210-1_6
DO  - 10.2991/978-94-6463-210-1_6
ID  - Gao2023
ER  -