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An algorithm design of Kansei recommender system

Lin, Pei-Chun and Arbaiy, Nureize (2018) An algorithm design of Kansei recommender system. In: Advances in Intelligent Systems and Computing. Recent Advances on Soft Computing and Data Mining, 700 . Springer, pp. 115-123. ISBN 9783319725505

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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.

Item Type: Book Section
Uncontrolled Keywords: Kansei engineering; fuzzy set theory; statistical modeling; recommender system; classifier; factor analysis; algorithm design
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Jul 2019 00:59
Last Modified: 31 Jul 2019 00:59
URI: http://eprints.uthm.edu.my/id/eprint/11371
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