Big Data Analysis of Personalized Recommendation in E-Commerce

DOI

10.2991/assehr.k.220401.147How to use a DOI?

Keywords

Principal Component Analysis; Association Analysis; Super Vector Machine; E-commence; Users’ Behavior

Abstract

The personalized recommendation analysis for users’ behaviors in e-commerce platform by big data analysis. Study on what kind of factors will affect on personal recommendations for goods on Amazon more effective by using Principal Component Analysis (PCA), Association Analysis and SVM (Super Vector Machine) Model. Using RFM Model to increase platform sales by analysis those data. This model aims to improve the accuracy and effectiveness of personalized recommendation, therefore improving the sales of goods on platform and the satisfaction of users.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Xijun Lin
PY  - 2022
DA  - 2022/04/08
TI  - Big Data Analysis of Personalized Recommendation in E-Commerce
BT  - Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)
PB  - Atlantis Press
SP  - 768
EP  - 771
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220401.147
DO  - 10.2991/assehr.k.220401.147
ID  - Lin2022
ER  -