The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: An application of smartphone recommendation

Yuen, Kevin Kam Fung ORCID: 0000-0003-1497-2575 (2017) The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: An application of smartphone recommendation. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 61. pp. 136-151.

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Official URL: http://dx.doi.org/10.1016/j.engappai.2017.02.001

Abstract

In a competitive high-end product market, many enterprises offer a variety of products to compete the market shares in different segments. Due to rich information of plenty of competitive product alternatives, consumers face the challenges to compare and choose the most suitable products. Whilst a product comprises different tangible and intangible features, consumers tend to buy the features rather than a product itself. A successful product has most features meeting the consumer needs. Perception values of product features from consumers are complex to be measured and predicted. To reduce information overload for searching their preferred products, this paper proposes the Fuzzy Cognitive Pairwise Comparison for Ranking and Grading Clustering (FCPC-RGC) to build a recommender system. The fuzzy number enables rating flexibility for the users to handle rating uncertainty. The Fuzzy Cognitive Pairwise Comparison (FCPC) is used to evaluate consumer preferences for multiple features of a product by pairwise comparison ratings. The Fuzzy Grade Clustering (FGC) is used to group the product alternatives into different consumer preference grades. To verify the validity and applicability of FCPC-RGC, a smartphone recommender system using the proposal approach is demonstrated how the system is able to help the consumers to recommend the suitable products according to the customers’ individual preference.

Item Type:	Article
Uncontrolled Keywords:	Recommender system, Information retrieval, Product recommendation, Fuzzy theory, Pairwise comparisons, Clustering, Decision making, User experience
Depositing User:	Symplectic Admin
Date Deposited:	06 Jul 2017 14:27
Last Modified:	19 Jan 2023 07:00
DOI:	10.1016/j.engappai.2017.02.001
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3008356