Maximal Marginal Relevance-Based Recommendation for Product Customisation

Wu, C.H., Wang, Y. and Ma, Jie (2023) Maximal Marginal Relevance-Based Recommendation for Product Customisation. Enterprise Information Systems, 17 (5). p. 1992018. ISSN 1751-7575

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Customised product design is attracting increasing attention. However, consumers can be overwhelmed by the variety of products. To confront this challenge, this paper presents a two-step recommendation approach for customised products. First, an adaptive specification process captures customer requirements in an accelerated manner by presenting the most informative attribute for a customer to specify. Then, a maximal marginal relevance-based recommendation set is presented, based on the customer’s partial specifications. This process ensures broad coverage of customers’ needs by considering not only the relevance of each product to their requirements but also redundancy in the recommendation set.

Item Type: Article
Additional Information: Funding information: This work was supported by the Hong Kong Research Grants Council Faculty Development Scheme [UGC/FDS14/E06/18]; Research Grants Council, University Grants Committee [RMGS-700026,UGC/FDS14/E06/18].
Uncontrolled Keywords: Customisation, product recommendation, probability relevance model
Subjects: N100 Business studies
N500 Marketing
Department: Faculties > Business and Law > Newcastle Business School
Depositing User: John Coen
Date Deposited: 02 Nov 2021 11:06
Last Modified: 02 Jun 2023 09:30

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