Personalised recommendation algorithm for social network based on two-dimensional correlation
by Aimei Zhu
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 13, No. 2, 2020

Abstract: In order to recommend friends in a real sense based on the personalised needs of users. A personalised recommendation algorithm based on two-dimensional correlation (FRBOT) was proposed for social network. In the proposed model, the interest similarity and trust relationship among users were combined with probability matrix decomposition to analyse the potential factor characteristics of the same preferences of selected trust users and target users. Compared with general matrix decomposition algorithm and personalised recommendation method based on user trust, the algorithm has evident advantages and can improve user satisfaction. The experimental results show that the performance of the proposed friend recommendation method is significantly improved compared with that of the existing friend recommendation methods.

Online publication date: Thu, 24-Sep-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Autonomous and Adaptive Communications Systems (IJAACS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com