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Kansei Evaluation of Product Recommendation Based on a Partial Comparison Process

Kansei Evaluation of Product Recommendation Based on a Partial Comparison Process

Jing-Zhong Jin, Yoshiteru Nakamori
Copyright: © 2014 |Pages: 15
ISBN13: 9781466660427|ISBN10: 1466660422|EISBN13: 9781466660434
DOI: 10.4018/978-1-4666-6042-7.ch073
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MLA

Jin, Jing-Zhong, and Yoshiteru Nakamori. "Kansei Evaluation of Product Recommendation Based on a Partial Comparison Process." Computational Linguistics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 1480-1494. https://doi.org/10.4018/978-1-4666-6042-7.ch073

APA

Jin, J. & Nakamori, Y. (2014). Kansei Evaluation of Product Recommendation Based on a Partial Comparison Process. In I. Management Association (Ed.), Computational Linguistics: Concepts, Methodologies, Tools, and Applications (pp. 1480-1494). IGI Global. https://doi.org/10.4018/978-1-4666-6042-7.ch073

Chicago

Jin, Jing-Zhong, and Yoshiteru Nakamori. "Kansei Evaluation of Product Recommendation Based on a Partial Comparison Process." In Computational Linguistics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1480-1494. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6042-7.ch073

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Abstract

This paper aims to find a new evaluation method for collecting Kansei and Context data, which is based on a partial comparison process; and a specification method based on customer's target, which is suitable for the special Kansei and Context data obtained from partial comparison process. For collecting Kansei and Context data, we randomly select 5 objects from all objects, and ask people to compare them on each attribute. After many times comparisons, many comparison lists will be obtained. With these lists, we map them into a directed graphic, and with using some graphic processing techniques, we combine all the comparison lists into a whole list without any contradictions, and we map the whole list into a certain range as our evaluated data. To access these special Kansei and Context data, we also discussed two specification methods based on semantic differential method. To test the new method on collecting Kansei data and the specification method, a comparison system and a recommendation system are developed.

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