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Document details

Title

Customized products recommendation based on probabilistic relevance model

Author

Yue Wang and Mitchell Tseng

Uploaded by

Yue Wang on 9/16/2013

Status

published

Category

Academic Papers

Industry

No specific industry

Content Focus

Company related – choice navigation

Keywords

configurator, Product design · Customized product recommendation · Probability ranking principle · Probability relevance model

Abstract

Product customization is attracting more attentions in industry as a viable strategy to better meet customer requirements and gain more profit. However the vast number of product variants in product customization process often makes it difficult for consumers to make purchase decisions, a phenomenon referred to as information overload. In this paper we take a two-prong approach to tackle the issue of information overload in customized products recommendation. Basically, the method answers two questions, namely, which products to recommend and in what order to present the recommendations. Firstly, a probability relevance model is deployed to calculate the probability of relevance for each end product. Then a probability ranking principle is exploited to present the recommendations. The approach also takes customer flexibility into consideration and thus mitigates the effect of inconsistent specifications from customers. It does not require any prior knowledge about an active customer’s preference and can accommodate the new customers challenge facing by recommendation approaches. Analytical results show that the method is optimal in terms of customer’s utility and product recommendation efficiency. Numerical experiments are also conducted to test the presented approach.

License

Bibliography

J Intell Manuf (2013) 24:951–960

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