Recommendation of items using a social-based collaborative filtering approach and classification techniques
by Lamia Berkani
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 13, No. 1/2, 2021

Abstract: With the large amount of data generated every day in social networks, the use of classification techniques becomes a necessity. The clustering-based approaches reduce the search space by clustering similar users or items together. We focus in this paper on the personalised item recommendation in social context. Our approach combines in different ways the social filtering algorithm and the traditional user-based collaborative filtering algorithm. The social information is formalised by some social-behaviour metrics such as friendship, commitment and trust degrees of users. Moreover, two classification techniques are used: an unsupervised technique applied initially to all users and a supervised technique applied to newly added users. Finally, the proposed approach has been experimented using different existing datasets. The obtained results show the contribution of integrating social information on the collaborative filtering and the added value of using the classification techniques on the different algorithms in terms of the recommendation accuracy.

Online publication date: Tue, 09-Feb-2021

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 Data Mining, Modelling and Management (IJDMMM):
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