REFERS: refined and effective fuzzy e-commerce recommendation system
by Sankar Pariserum Perumal; Ganapathy Sannasi; Kannan Arputharaj
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 17, No. 1, 2020

Abstract: Online shopping culture is gaining traction globally and some of the biggest beneficiaries of this e-commerce shift are Amazon, eBay, etc. Recommendation systems guide online users in a personalised manner to choose what they want and their interest on each product present in the catalogue list. In such a scenario, the existing systems need complete information for making recommendations, which is not always possible in real applications. Therefore, a novel refined and effective fuzzy e-commerce recommendation system has been proposed in this paper that combines the benefits of difference in importance within the rating factors by a single user and new similarity measure approach that aims at improved recommendation list to the e-commerce user. The proposed methodology has been implemented using a new similarity measure on experimental datasets and the refined scores for such e-commerce website-based unlocked mobile phones are compared in this work against classic similarity measures.

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