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Understanding consumer preference through fuzzy-based recommendation system

•Our recommendation technique explicates users’ innate associations between the attributes.•It takes both linguistic and numerical data by using fuzzy statistics.•Allows to measure predictive power of one attribute for any other attribute.•It provides a flexibility in choosing attributes as dependan...

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Bibliographic Details
Published in:IIMB management review 2021-12, Vol.33 (4), p.287-298
Main Authors: Mandal, Madhu, Mohanty, B.K., Dash, Satyabhusan
Format: Article
Language:English
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Summary:•Our recommendation technique explicates users’ innate associations between the attributes.•It takes both linguistic and numerical data by using fuzzy statistics.•Allows to measure predictive power of one attribute for any other attribute.•It provides a flexibility in choosing attributes as dependant and independent variables.•It allows articulation of buyers’ trade-offs amongst attributes of a product. We introduce a new recommendation technique based on fuzzy concepts such as the 2-tuple fuzzy linguistic representation and the fuzzy market research system approach. The recommendation technique uses general consumer perception about product attributes of multi-attributed products such as smartphones as input and gives consumers’ sequential preference of the products as output. The recommendation technique explains the power of an attribute in predicting other attributes, especially in fuzzy and uncertain environments, through the conceptualisation of consumers’ innate associations between the attributes. Our technique provides an understanding of consumer preference through the articulation of buyers’ trade-offs amongst attributes of a product.
ISSN:0970-3896
DOI:10.1016/j.iimb.2021.03.015