Research on collaborative recommendation of multidimensional sparse data based on personalised directional information fusion algorithm Online publication date: Fri, 22-Jan-2021
by Yi Huang
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 13, No. 4, 2020
Abstract: In order to solve the problem of poor precision of recommendation results in the traditional collaborative recommendation method for multidimensional sparse data, a collaborative recommendation method for multidimensional sparse data based on personalised directional information fusion algorithm was proposed. The cosine similarity of the data vector was calculated and modified, and the score prediction set was established. On the basis of the predicted value and variable quantity value, the median, mean and model were used to populate and deconstruct the standard representative data, construct the multiple score matrix, solve the data location problem of sparse matrix, and realise the data collaborative recommendation. The experimental results show that the average error of the research method is about 0.03, lower than the traditional method, which proves that the method can effectively improve the accuracy of data recommendation.
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 Autonomous and Adaptive Communications Systems (IJAACS):
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