Maximal marginal relevance-based recommendation for product customisation

C. H. WU, Yue WANG, J. MA

Research output: Contribution to journalArticlespeer-review

12 Citations (Scopus)

Abstract

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. Copyright © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Original languageEnglish
Article number1992018
JournalEnterprise Information Systems
Volume17
Issue number5
Early online dateOct 2021
DOIs
Publication statusPublished - 2023

Citation

Wu, C. H., Wang, Y., & Ma, J. (2023). Maximal marginal relevance-based recommendation for product customisation. Enterprise Information Systems, 17(5), Article 1992018. https://doi.org/10.1080/17517575.2021.1992018

Keywords

  • Customisation
  • Product recommendation
  • Probability relevance model

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