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

• Published in: Journal of intelligent manufacturing Vol. 24; no. 2; pp. 251 - 265
• Main Authors: Mavridou, Efthimia, Kehagias, Dionisis D., Tzovaras, Dimitrios, Hassapis, George
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2013; Springer Nature B.V
• Subjects: Advanced manufacturing technologies; Architecture; Automobile industry; Automobiles; Automotive components; Automotive engineering; Automotive industry; Business and Management; Co-design; Collaboration; Control; Customers; Customization; Customizing; Data mining; Electronic commerce; Engineering; Machine learning; Machines; Manufacturing; Mechatronics; Mining; Processes; Production; Recommender systems; Robotics; Studies; Affective needs; Mass customization; Data mining; Recommender systems; Automotive configurators
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-011-0579-4

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.