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A personalized requirement identifying model for design improvement based on user profiling

Published online by Cambridge University Press:  27 November 2019

Jing Li Open the ORCID record for Jing Li [Opens in a new window] ,
Xinwei Zhang ,
Keqin Wang ,
Chen Zheng ,
Shurong Tong  and
Benoit Eynard
Jing Li*
Affiliation:
Institute of Design Management, Northwestern Polytechnical University, Xi'an, China
Xinwei Zhang
Affiliation:
Institute of Design Management, Northwestern Polytechnical University, Xi'an, China
Keqin Wang
Affiliation:
Institute of Design Management, Northwestern Polytechnical University, Xi'an, China
Chen Zheng
Affiliation:
Mechanical Engineering School, Northwestern Polytechnical University, Xi'an, China
Shurong Tong
Affiliation:
Institute of Design Management, Northwestern Polytechnical University, Xi'an, China
Benoit Eynard
Affiliation:
Department of Mechanical Systems Engineering – UMR 7337 Roberval, Sorbonne Universités, Université de Technologie de Compiègne, Compiègne Cedex, France
*
Author for correspondence: Jing Li, E-mail: [email protected]
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Abstract

The personalization of products and services has become an inevitable trend in the manufacturing and service industry, but it is very difficult to identify users' personalized requirements accurately. This paper solves this problem by constructing an identifying model for personalized requirement based on user profiling. Firstly, the framework of the proposed model and the process of identifying the user's personalized requirements with this model are introduced, and then an experimental scheme for obtaining users' profiling data is designed. On this basis, an experiment is performed by investigating users' requirements for the computer to obtain the data, and the data are used for the analysis based on the proposed model. The analysis result shows that the model can reveal the difference among heterogeneous users well, find out the implicit requirements of users, and identify the gap between existing products and users' personalized requirements, which provides support to the subsequent improvement of product design.

Keywords

Design improvementpersonalized requirementrequirement identifyinguser profiling
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 1: Thematic Collection on “Cognitive and Learning processes for transition to Design for Sustainability (DfS)” , February 2020 , pp. 55 - 67
Copyright
Copyright © Cambridge University Press 2019

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References

Bamford, DR and Greatbanks, RW (2005) The use of quality management tools and techniques: a study of application in everyday situations. International Journal of Quality & Reliability Management 22, 376–392.10.1108/02656710510591219CrossRefGoogle Scholar
Blei, D, Ng, A and Jordan, M (2003) Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022Google Scholar
Boukhari, I, Bellatreche, L and Jean, S (2012) An ontological pivot model to interoperate heterogeneous user requirements. Lecture Notes in Computer Science 7610, 344–358.10.1007/978-3-642-34032-1_35CrossRefGoogle Scholar
Chung, TS, Rust, RT and Wedel, M (2009) My mobile music: an adaptive personalization system for digital audio players. Marketing Science 28, 52–68.10.1287/mksc.1080.0371CrossRefGoogle Scholar
Daoud, M, Tamine, L and Boughanem, M (2010) A personalized graph-based document ranking model using a semantic user profile. 18th International Conference on User Modeling, Adaptation, and Personalization, Big Island, HI, USA, June 20th–24th, 2010.10.1007/978-3-642-13470-8_17CrossRefGoogle Scholar
Davenport, T, Mule, LD and Lucker, J (2011) Know what your customers want before they do. Harvard Business Review 89, 84–92.Google Scholar
Greenyer, J, Haase, M, Marhenke, J and Bellmer, R (2015) Evaluating a formal scenario-based method for the requirements analysis in automotive software engineering. ACM 10th Joint Meeting on Foundations of Software Engineering, Bergamo, Italy, August 30th–September 4th, 2015.10.1145/2786805.2804432CrossRefGoogle Scholar
Guo, F, Liu, W, Cao, Y, Liu, F and Li, M (2015) Optimization design of a webpage based on Kansei Engineering. Human Factors and Ergonomics in Manufacturing & Service Industries 26, 110–126.10.1002/hfm.20617CrossRefGoogle Scholar
Guo, L, Li, Y, Mu, D, Yang, T and Li, Z (2016) A LDA model based topic detection method. Journal of Northwestern Polytechnic University 34, 697–701.Google Scholar
Hauser, JR, Urban, GL, Liberali, G and Braun, M (2009) Website morphing. Marketing Science 28, 202–223.10.1287/mksc.1080.0459CrossRefGoogle Scholar
Hauser, JR, Liberali, G(G) and Urban, GL (2014) Website morphing 2.0: switching costs, partial exposure, random exit, and when to morph. Management Science 60, 1594–1616.10.1287/mnsc.2014.1961CrossRefGoogle Scholar
He, L, Ming, X, Li, M, Zheng, M and Zhitao, X (2015) Understanding customer requirements through quantitative analysis of an improved fuzzy Kano's model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, 699–711.10.1177/0954405415598894CrossRefGoogle Scholar
Hull, E, Jackson, K and Dick, J (2011) Requirements Engineering. London: Springer.10.1007/978-1-84996-405-0CrossRefGoogle Scholar
Jiao, J(R) and Chen, C-H (2014) Customer requirement management in product development: a review of research issues. Concurrent Engineering 14, 173–185.10.1177/1063293X06068357CrossRefGoogle Scholar
Jiao, J, Tseng, MM and Du, X (2003) Identifying need patterns for customization and personalization. Integrating Manufacturing Systems 14, 387–396.Google Scholar
Kesorn, K, Liang, Z and Poslad, S (2009) Use of granularity and coverage in a user profile model to personalise visual content retrieval. IEEE 2009 Second International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies, and Services (CENTRIC), Porto, Portugal, September 20th–25th, 2009.10.1109/CENTRIC.2009.19CrossRefGoogle Scholar
Kreutler, G and Jannach, D (2006) Personalized needs elicitation in web-based configuration systems. In Thorsten, B and Gerhard, F (eds), Mass Customization: Challenges and Solutions. Berlin, Germany: Springer Press, pp. 27–42.10.1007/0-387-32224-8_2CrossRefGoogle Scholar
Miceli, G, Ricotta, F and Costabile, M (2007) Customizing customization: a conceptual framework for interactive personalization. Journal of Interactive Marketing 21, 6–25.10.1002/dir.20076CrossRefGoogle Scholar
Nagamachi, M (2016) Kansei/Affective Engineering, 1st edn. Boca Raton, FL: CRC Press.10.1201/EBK1439821336CrossRefGoogle Scholar
Nagamachi, M and Imada, AS (1995) Kansei Engineering: an ergonomic technology for product development. International Journal of Industrial Ergonomics 15, 1–1.10.1016/0169-8141(95)90025-XCrossRefGoogle Scholar
Ouaftouh, S, Zellou, A and Idri, A (2015) User profile model: a user dimension based classification. IEEE 10th International Conference on Intelligent Systems: Theories and Applications (SITA), Rabat, Morocco, October 20th–21st, 2015.10.1109/SITA.2015.7358378CrossRefGoogle Scholar
Shieh, MD, Yan, W and Chen, CH (2008) Soliciting customer requirements for product redesign based on picture sorts and ART2 neural network. Expert Systems with Applications 34, 194–204.10.1016/j.eswa.2006.08.036CrossRefGoogle Scholar
Sousa-Zomer, TT and Miguel, PAC (2017) A QFD-based approach to support sustainable product-service systems conceptual design. The International Journal of Advanced Manufacturing Technology 88, 701–717.10.1007/s00170-016-8809-8CrossRefGoogle Scholar
Stormer, H (2009) Improving product configurators by means of a collaborative recommender system. International Journal of Mass Customisation 3, 165–178.CrossRefGoogle Scholar
Su, J, Jiang, P, Zhu, B and Li, H (2004) Research on Kansei Engineering and its application to product design. Journal of Xi'an Jiaotong University 38, 60–63.Google Scholar
Trusov, M, Ma, L and Jamal, Z (2016) Crumbs of the cookie: user profiling in customer-base analysis and behavioral targeting. Marketing Science 35, 405–426.CrossRefGoogle Scholar
Tseng, MM and Hu, JS (2014) Mass customization. Berlin/Heidelberg: Springer.Google Scholar
Vezzoli, C, Ceschin, F, Osanjo, L, Mugendi, MK and Diehl, JC (2018) Design for Sustainability: An Introduction. London: Springer.CrossRefGoogle Scholar
Von Hippel, E and Katz, R (2002) Shifting innovation to users via toolkits. Management Science 48, 821–833.10.1287/mnsc.48.7.821.2817CrossRefGoogle Scholar
Vu, T, Willis, A, Kruschwitz, U and Song, D (2017) Personalised query suggestion for intranet search with temporal user profiling. ACM 17th Proceedings of the 2017 Conference on Human Information Interaction and Retrieval, Oslo, Norway, March 7th–11th, 2017.CrossRefGoogle Scholar
Yu, W, Wang, J, Wei, X and Li, R (2008) A new product configuration approach based on BOM (bill of material). Mechanical Science and Technology for Aerospace Engineering 27, 446–450.Google Scholar
Zhao, J and Yan, X (2015) A service process model for outsourcing human resources management system: based on massive customized requirements research perspectives. The Open Cybernetics and Systemics Journal 9, 1496–1501.CrossRefGoogle Scholar