Title: REFERS: refined and effective fuzzy e-commerce recommendation system

Authors: Sankar Pariserum Perumal; Ganapathy Sannasi; Kannan Arputharaj

Addresses: Department of Information Science and Technology, College of Engineering Campus, Anna University, Chennai, 600025, Tamil Nadu, India ' Department of Computing Science, VIT Chennai Campus, Chennai, Tamil Nadu, India ' Department of Information Science and Technology, College of Engineering Campus, Anna University, Chennai, 600025, Tamil Nadu, India

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.

Keywords: fuzzy recommendation system; degree of similarity measure; rating factor importance; collective expert rating.

DOI: 10.1504/IJBIDM.2020.108031

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.1, pp.117 - 137

Received: 01 Nov 2017
Accepted: 22 Dec 2017
Published online: 02 Jul 2020 *

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