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

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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.

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Authors and Affiliations

  1. Centre for Research and Technology Hellas, Informatics and Telematics Institute, 6th km Charilaou—Thermi Rd., 57001, Thermi, Thessaloniki, Greece

    Efthimia Mavridou, Dionisis D. Kehagias & Dimitrios Tzovaras

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    George Hassapis

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  1. Efthimia Mavridou
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  2. Dionisis D. Kehagias
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  3. Dimitrios Tzovaras
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  4. George Hassapis
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Correspondence to Efthimia Mavridou.

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Mavridou, E., Kehagias, D.D., Tzovaras, D. et al. Mining affective needs of automotive industry customers for building a mass-customization recommender system. J Intell Manuf 24, 251–265 (2013). https://doi.org/10.1007/s10845-011-0579-4

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  • DOI: https://doi.org/10.1007/s10845-011-0579-4

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