Information Retrieval
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
Configuration-Design Problem Solving
IEEE Expert: Intelligent Systems and Their Applications
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
A Configuration Tool to Increase Product Competitiveness
IEEE Intelligent Systems
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Process configuration: Combining the principles of product configuration and process planning
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Generative constraint-based configuration of large technical systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Feature Selection via Coalitional Game Theory
Neural Computation
Towards a generic model of configuraton tasks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Applying case-based reasoning for product configuration in mass customization environments
Expert Systems with Applications: An International Journal
Dynamic constraint satisfaction problems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Configurators have been generally accepted as important tools to elicit customers' needs and find the matches between customers' requirements and company's offerings. With product configurators, product design is reduced to a series of selections of attribute values. However, it has been acknowledged that customers are not patient enough to configure a long list of attributes. Therefore, making every round of configuring process productive and hence reducing the number of inputs from customers are of substantial interest to academic and industry alike. In this paper, we present an efficient product configuration approach by incorporating Shapley value, which is a concept used in game theory, to estimate the usefulness of each attribute in the configurator design. This new method iteratively selects the most relevant attribute that can contribute most in terms of information content from the remaining pool of unspecified attributes. As a result from product providers' perspective, each round of configuration can best narrow down the choices with given amount of time. The selection of the next round query is based on the customer's decision on the previous rounds. The interactive process thus runs in an adaptive manner that different customers will have different query sequences. The probability ranking principle is also exploited to give product recommendation to truncate the configuration process so that customers will not be burdened with trivial selection of attributes. Analytical results and numerical examples are also used to exemplify and demonstrate the viability of the method.