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Maximal Marginal Relevance-Based Recommendation for Product Customisation
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AI
DOI：10.1080/17517575.2021.1992018.png)
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Abstract
En 中文
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.
Keywords:
Customisation
product recommendation
probability relevance model
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