A personalised recommendation algorithm of user preference products based on Bayesian network
by Hongli Wan; Yuchen Li
International Journal of Product Development (IJPD), Vol. 25, No. 2, 2021

Abstract: In order to overcome the problems of low recommendation accuracy, coverage rate and user diversity index in current personalised recommendation algorithms for user preference products, a new personalised recommendation algorithm based on Bayesian network is proposed. The algorithm takes into account the changing rule of users' interest characteristics with time, and divides the friendly neighbour network. The tags that users are interested in are obtained by user tag information and network partition results, the user's preference for products is obtained by combining with Bayesian network, and personalised products are recommended for users according to the results of preference calculation. The simulation results show that the proposed algorithm can effectively increase the accuracy, coverage and diversity index of user preference products, and recommend the most satisfactory products for users.

Online publication date: Mon, 12-Jul-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 Product Development (IJPD):
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 subs@inderscience.com