Analysis model of the short-term search behaviour guidance of e-commerce platform users based on knowledge graph Online publication date: Wed, 10-Jan-2024
by Bin Li; Zhisheng Zhou
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 29, No. 3/4, 2023
Abstract: In order to solve the problems of poor satisfaction with product guidance and low user order rate in existing e-commerce platforms, a knowledge graph based short-term search behaviour guidance analysis model for e-commerce platform users is proposed. Firstly, construct a knowledge graph to collect samples of users' short-term search behaviour. Then, attribute preference weighting is applied to users' short-term search behaviour, and the optimal sequential search theory is introduced to construct a user short-term search behaviour guidance analysis model. Finally, matrix decomposition method is used to extract the features of users and products, achieving short-term search behaviour guidance analysis for users. The results show that after using the guidance analysis method in this article, user satisfaction with the product and user order rate can always reach over 90%, indicating good application performance.
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