Online products recommendation system using genetic kernel fuzzy C-means and probabilistic neural network
by E. Manohar; D. Shalini Punithavathani
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 17, No. 1, 2020

Abstract: The purchaser's review plays a significant role in choosing the purchasing activities for online shopping as a customer desires to obtain the opinion of other purchasers by observing their opinion through online products. However, most appropriate product selection from the best website is a challenging problem for online users. Accordingly, this paper proposes a hybrid recommendation system for identifying customer preferences and recommending the most appropriate product. To do this, first the dataset is collected and prepared in the pre-processing step. Genetic kernel fuzzy C-means (GAKFCM) is used for usage cluster formation after the pre-processing step. The different features are extracted from each cluster-based user interest level. The user interest levels are used as features for classifier to extract user knowledge discovery. Based upon the user interest level, the product recommendation is done using probabilistic neural network (PNN). The simulation results show high precision rate which clearly indicates that the proposed method is very useful and appealing.

Online publication date: Thu, 02-Jul-2020

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 Business Intelligence and Data Mining (IJBIDM):
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