Study on news recommendation of social media platform based on improved collaborative filtering Online publication date: Thu, 15-Feb-2024
by Bin Wu
International Journal of Web Based Communities (IJWBC), Vol. 20, No. 1/2, 2024
Abstract: Aiming at the problems of low recommendation accuracy and low user interest in the existing methods, a news recommendation of social media platform based on improved collaborative filtering is designed. The initial key features of news data are determined, and the occurrence frequency of key features is counted by chi square, so as to realise feature extraction. First, we calculate the mutual information between different news data features, determine the correlation degree between features, and remove the data with similar features and low correlation degree. Then, the collaborative filtering algorithm is improved by adding timing update, trust and other data in collaborative filtering. Finally, the improved collaborative filtering algorithm is used to build a recommendation model, and the news data characteristics and user preference data are input into the model to complete the recommendation. The experimental results show that the news data recommended by the proposed method has high accuracy and high user interest.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Web Based Communities (IJWBC):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook