An Algorithm Design of Kansei Recommender System

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


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.


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



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.


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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

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