Skip to main content
SpringerLink
Log in
Book cover

International Conference on Soft Computing and Data Mining

SCDM 2018: Recent Advances on Soft Computing and Data Mining pp 115–123Cite as

An Algorithm Design of Kansei Recommender System

  • Conference paper
  • First Online:

  • 1373 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 700))

Abstract

We propose an algorithm design for a Recommender System based on a Kansei model in this paper, we called this algorithm as Kansei Recommender System (hereafter, we denoted as KRS algorithm). The purpose of KRS algorithm is to support designers to pre-know the appearance feeling (Kansei) of products from consumers. To complete this algorithm, we divide the algorithm design into three parts: (1) Extract Kansei factors and evaluation factors from consumers’ shopping items. (2) Determine a Kansei model for KRS algorithm. (3) Making decision by using KRS algorithm. We also give a concept map of paradigm by using KRS algorithm. In conclusion, we remain the future work to implement the KRS algorithm in real case studies with different fields of enterprises.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Chuan, N.K., Sivaji, A., Shahimin, M.M., Saad, N.: Kansei Engineering for e-commerce sunglasses selection in Malaysia. Proc. Soc. Behav. Sci. 97, 707–714 (2013)

    Article  Google Scholar 

  2. Cao, Y., Li, Y.: An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst. Appl. 33, 230–240 (2007)

    Article  Google Scholar 

  3. Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th IEEE, pp. 271–280 (2007)

    Google Scholar 

  4. Hotta, H., Hagiwara, M.: A fuzzy rule based personal Kansei modeling system. In: 2006 IEEE International Conference on Fuzzy Systems, Vancouver, Canada, 16–21 July, pp. 1031–1037 (2006)

    Google Scholar 

  5. Huang, M.S., Tsai, H.C., Lai, W.W.: Kansei Engineering applied to the form design of injection molding machines. Open J. Appl. Sci. 2, 198–208 (2012)

    Article  Google Scholar 

  6. He, Z.X., Wang, S.: Mapping customer requirements to product performance index based on data fusion by vague set. J. Comput. Inf. Syst. 6, 1679–1686 (2009)

    Google Scholar 

  7. Jindo, T., Hirasago, K., Nagamachi, M.: Development of a design support system for office chairs using 3-D graphics. Int. J. Ind. Ergon. 15(1), 49–62 (1995)

    Article  Google Scholar 

  8. Lokman, A.M., Nagamachi, M.: Kansei Engineering: a beginner’s perspective. UPENA, Malaysia (2010)

    Google Scholar 

  9. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering, Internet Comput. IEEE 7, 76–80 (2003)

    Google Scholar 

  10. Lu, H., Yan, C., Du, J.: An interactive system based on Kansei Engineering to support clothing design process. Res. J. Appl. Sci. Eng. Technol. 6(24), 4531–4535 (2013)

    Google Scholar 

  11. Lin, P.-C., Nureize, A., Hsiao, Y.-C.: Hypothesis test for identifying the vague factors from consolidated income. In: 2017 IEEE International Conference on Fuzzy Systems, Naples, Italy, 9–12 July 2017

    Google Scholar 

  12. Lin, P.-C., Nureize, A.: One-way ANOVA model with fuzzy data for distinguishing factors from consumer demand. In: Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol. 549, pp. 111–121 (2017)

    Google Scholar 

  13. Lin, P.-C., Watada, J., Wu, B.: A parametric assessment approach to solving facility location problems with fuzzy demands. IEEJ Trans. Electron. Inf. Syst. 9(5), 484–493 (2014)

    Google Scholar 

  14. Lin, P.-C., Nureize, A.: Two-echelon logistic model based on game theory with fuzzy variable. In: Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol. 287, pp. 325–334 (2014)

    Google Scholar 

  15. Lin, P.-C., Watada, J., Wu, B.: Risk assessment of a portfolio selection model based on a fuzzy statistical test. IEICE Trans. Inf. Syst. E96-D(3), 579–588 (2013)

    Google Scholar 

  16. Lin, P.-C., Watada, J., Wu, B.: Identifying the distribution difference between two populations of fuzzy data based on a nonparametric statistical method. IEEJ Trans. Electron. Inf. Syst. 8(6), 591–598 (2013)

    Google Scholar 

  17. Lin, P.-C., Watada, J., Wu, B.: Portfolio selection model with interval values base on fuzzy probability distribution functions. Int. J. Innov. Comput. Inf. Control 8(8), 5935–5944 (2012)

    Google Scholar 

  18. Lin, P.-C., Wu, B., Watada, J.: Goodness-of-fit test for membership functions with fuzzy data. Int. J. Innov. Comput. Inf. Control 8(10), 7437–7450 (2012)

    Google Scholar 

  19. Lin, P.-C., Watada, J., Wu, B.: A database for a new fuzzy probability distribution function and its application. Int. J. Innov. Manag. Inf. Prod. 2(2), 1–7 (2011)

    Google Scholar 

  20. Lin, P.-C., Wu, B., Watada, J.: Kolmogorov-Smirnov two sample test with continuous fuzzy data. Integr. Uncertain. Manag. Appl. 68, 175–186 (2010)

    Article  MATH  Google Scholar 

  21. Nagamachi, M.: Introduction of Kansei Engineering. Standard Association, Tokyo, Japan (1996)

    Google Scholar 

  22. Nagamachi, M.: Kansei Engineering as a powerful consumer-oriented technology for product development. Appl. Ergon. 33(3), 289–294 (2002)

    Article  Google Scholar 

  23. Nagamachi, M., Matsubara, Y.: Hybrid Kansei Engineering system and design support. Int. J. Ind. Ergon. 19(2), 81–92 (1997)

    Article  Google Scholar 

  24. Nureize, A., Lin, P.-C.: Weighted value assessment of linear fractional programming for possibilistic multi-objective problem. Int. J. Adv. Intell. Paradig. 8(1), 42–58 (2016)

    Article  Google Scholar 

  25. Nureize, A., Watada, J., Lin, P.-C.: Fuzzy random regression-based modeling in uncertain environment. In: Sustaining Power Resources through Energy Optimization and Engineering, pp. 127–146. IGI Global (2016)

    Google Scholar 

  26. Nureize, A., Watada, J.: Building multi-attribute decision model based on Kansei Information in environment with hybrid uncertainty. In: Intelligent Decision Technologies, pp. 103–112. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  27. Nagamachi, M.: An image technology expert system and its application to design consultation. Int. J. Hum.-Comput. Interact. 3(3), 267–279 (1991)

    Article  MathSciNet  Google Scholar 

  28. Nagamachi, M.: Kansei Engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15(1), 3–11 (1995)

    Article  Google Scholar 

  29. Ozaki, S., Hisano, S., Iwamoto, Y.: Potency of animal models in Kansei Engineering. Kansei Eng. Int. J. 11, 127–132 (2012)

    Article  Google Scholar 

  30. Sivaji, A., Downe, A.G., Mazlan, M.F., Soo, S., Abdullah, A.: Importance of incorporating fundamental usability with social and trust elements for e-commerce website. In: Proceedings of the Business, Engineering and Industrial Applications (ICBEIA), pp. 221–6, Kuala Lumpur, Malaysia, 5–7 June 2011

    Google Scholar 

  31. Tanaka, M., Miyaji, M., Yamamoto, U., Hiroyasu, T., Miki, M.: Interactive recommender system to estimate personal user’s Kansei Model. Int. J. Comput. Sci. Eng. (IJCSE) 5(11), 904–913 (2013)

    Google Scholar 

  32. Tan, Z.Y., Sun, S.Q.: Image retrieval technology based on imagery cognition model. Eng. Sci. 42(5), 763–767 (2008)

    Google Scholar 

  33. Tang, Z., Sun, S., Zeng, X., Cao, H., Xing, B., Yang, Z.: Researching on Kansei Engineering system for product image survey and retrieval. J. Comput. Inf. Syst. 10(10), 4029–4038 (2014)

    Google Scholar 

  34. Yoshiki, N.: Kansei Data Analysis. Morikita Shuppan (2000)

    Google Scholar 

  35. Zhai, L.Y., Khoo, L.P., Zhao, W.Z.: A dominance-based rough set approach to Kansei Engineering in product development. Expert Syst. Appl. 6, 393–402 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

The authors express her appreciation to the University Tun Hussein Onn Malaysia (UTHM). This research also supported by GATES IT Solution Sdn. Bhd. Under its publication scheme.

Author information

Authors and Affiliations

  1. Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Rd., Seatwen, 40724, Taichung, Taiwan

    Pei-Chun Lin

  2. Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Nureize Arbaiy

Authors
  1. Pei-Chun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nureize Arbaiy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pei-Chun Lin .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  4. School of Information Technology, Deakin University, Geelong, Victoria, Australia

    Jemal H. Abawajy

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lin, PC., Arbaiy, N. (2018). An Algorithm Design of Kansei Recommender System. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72550-5_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72549-9

  • Online ISBN: 978-3-319-72550-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation