An Algorithm Design of Kansei Recommender System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Kansei Engineering Fuzzy set theory Statistical modeling Recommender system Classifier Factor analysis Algorithm design 

Notes

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.

References

  1. 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)CrossRefGoogle Scholar
  2. 2.
    Cao, Y., Li, Y.: An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst. Appl. 33, 230–240 (2007)CrossRefGoogle Scholar
  3. 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. 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. 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)CrossRefGoogle Scholar
  6. 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. 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)CrossRefGoogle Scholar
  8. 8.
    Lokman, A.M., Nagamachi, M.: Kansei Engineering: a beginner’s perspective. UPENA, Malaysia (2010)Google Scholar
  9. 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. 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. 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 2017Google Scholar
  12. 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. 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. 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. 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. 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. 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. 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. 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. 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)CrossRefMATHGoogle Scholar
  21. 21.
    Nagamachi, M.: Introduction of Kansei Engineering. Standard Association, Tokyo, Japan (1996)Google Scholar
  22. 22.
    Nagamachi, M.: Kansei Engineering as a powerful consumer-oriented technology for product development. Appl. Ergon. 33(3), 289–294 (2002)CrossRefGoogle Scholar
  23. 23.
    Nagamachi, M., Matsubara, Y.: Hybrid Kansei Engineering system and design support. Int. J. Ind. Ergon. 19(2), 81–92 (1997)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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. 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. 27.
    Nagamachi, M.: An image technology expert system and its application to design consultation. Int. J. Hum.-Comput. Interact. 3(3), 267–279 (1991)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Nagamachi, M.: Kansei Engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15(1), 3–11 (1995)CrossRefGoogle Scholar
  29. 29.
    Ozaki, S., Hisano, S., Iwamoto, Y.: Potency of animal models in Kansei Engineering. Kansei Eng. Int. J. 11, 127–132 (2012)CrossRefGoogle Scholar
  30. 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 2011Google Scholar
  31. 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. 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. 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. 34.
    Yoshiki, N.: Kansei Data Analysis. Morikita Shuppan (2000)Google Scholar
  35. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Information Engineering and Computer ScienceFeng Chia UniversitySeatwenTaiwan
  2. 2.Faculty of Computer Science and Information TechnologyUniversity Tun Hussein Onn MalaysiaBatu PahatMalaysia

Personalised recommendations