A method for predicting consumer purchase intention in e-commerce in the era of social media Online publication date: Wed, 10-Jan-2024
by Aihua Mo
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 29, No. 3/4, 2023
Abstract: The research goal is to improve the AUC value and R2 value of the prediction results and reduce the average absolute error and propose a prediction method of e-commerce consumers' purchase intention in the era of social media. Firstly, linear transformation is performed on the historical purchasing data of consumers, and the importance of the data is determined through thresholds. Based on the judgment results, the data is dimensionally reduced. Secondly, the minimum hash algorithm is used to calculate the similarity between the dimensionality reduced data, and the fuzzy clustering decision method is used to classify historical purchase data. Finally, the Bayesian personalised sorting method is used to predict the purchase intention. The experimental results show that the AUC value and R2 value of the proposed method are large, and the average absolute error is low, indicates that the prediction effect of this method is good.
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 Networking and Virtual Organisations (IJNVO):
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