2022
DOI: 10.1155/2022/1761579
|View full text |Cite
|
Sign up to set email alerts
|

e-Commerce Personalized Recommendation Based on Machine Learning Technology

Abstract: As e-commerce offers more and more choices for users, its structure becomes more and more complicated. Inevitably, it brings about the problem of information overload. The solution to this problem is an e-commerce personalized recommendation system using machine learning technology. People often seem confused when facing extensive information and cannot grasp the key points. This paper studies the personalized recommendation technology of e-commerce: deeply analyzes the related technologies and algorithms of t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
3
2

Relationship

0
10

Authors

Journals

citations
Cited by 15 publications
(6 citation statements)
references
References 23 publications
0
6
0
Order By: Relevance
“…In 2022, Liu, L., et.al, [25] have presented e-commerce personalized recommendation depend on ML technology. Here, examines the e-commerce industry's personalized the recommendation technology.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In 2022, Liu, L., et.al, [25] have presented e-commerce personalized recommendation depend on ML technology. Here, examines the e-commerce industry's personalized the recommendation technology.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The paper entitled "e-Commerce Personalized Recommendation Based on Machine Learning Technology" by Liping Liu et al [18] delves into the realm of e-commerce personalized recommendations through the lens of machine learning technology. The author's work advances the rapidly developing field of improving online shopping systems' user experiences.…”
Section: IImentioning
confidence: 99%
“…The integrated optimization mathematical model of forward and reverse logistics of B2C distribution system with fuzzy random demand is as follows [21]:…”
Section: E-commerce Website Evaluation Model Based On Analytic Hierar...mentioning
confidence: 99%