Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mining affective needs of automotive industry customers for building a mass-customization... Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent Manufacturing Springer Journals

Mining affective needs of automotive industry customers for building a mass-customization recommender system

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media, LLC
Subject
Economics / Management Science; Production/Logistics/Supply Chain; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics
ISSN
0956-5515
eISSN
1572-8145
DOI
10.1007/s10845-011-0579-4
Publisher site
See Article on Publisher Site

Abstract

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.

Journal

Journal of Intelligent ManufacturingSpringer Journals

Published: Aug 6, 2011

References

  • Configuration for mass customization: How to extend product configuration towards requirements and process configuration
    Aldanondo, M.; Vareilles, E.
  • A lazy approach to associative classification
    Baralis, E.; Chiusano, S.; Garza, P.
  • Hybrid recommender systems: Survey and experiments
    Burke, R.

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