User evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation

Ryuya Akase, Yoshihiro Okada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The authors develop the user evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation (IEC) implemented by interactive genetic algorithm and interactive differential evolution. In addition, the facial expression generation system described in this paper simulates user evaluation based on personalized models and generates images of happy faces and sad faces automatically as an example. IEC that can optimize its targets according to the user’s preference and sensibility is attracting attention as an interactive personalization method. However, IEC has the problem of user evaluation fatigue because it requires a lot of user evaluations to search the optimum solution. Therefore, interactive systems employing IEC are used with a user evaluation prediction model so that they can reduce a user’s load. The novelties of this study are combination of conjoint analysis and large scale neural networks integrated with user evaluation prediction models. Finally, the authors verify usability of the proposed models by performing user evaluation experiments. As a result, the proposed models indicate better prediction accuracy of user evaluation than a previous research using a simple neural network. Also, the personalized models can simulate user evaluation successfully.

Original languageEnglish
Title of host publicationApplied Computing and Information Technology
EditorsRoger Lee
PublisherSpringer Verlag
Pages91-104
Number of pages14
ISBN (Print)9783319514710
DOIs
Publication statusPublished - Jan 1 2017
Event4th International Conference on Applied Computing and Information Technology, ACIT 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

NameStudies in Computational Intelligence
Volume695
ISSN (Print)1860-949X

Other

Other4th International Conference on Applied Computing and Information Technology, ACIT 2016
CountryUnited States
City Las Vegas
Period12/12/1612/14/16

Fingerprint

Evolutionary algorithms
Neural networks
Genetic algorithms
Fatigue of materials
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Akase, R., & Okada, Y. (2017). User evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation. In R. Lee (Ed.), Applied Computing and Information Technology (pp. 91-104). (Studies in Computational Intelligence; Vol. 695). Springer Verlag. https://doi.org/10.1007/978-3-319-51472-7_7

User evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation. / Akase, Ryuya; Okada, Yoshihiro.

Applied Computing and Information Technology. ed. / Roger Lee. Springer Verlag, 2017. p. 91-104 (Studies in Computational Intelligence; Vol. 695).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Akase, R & Okada, Y 2017, User evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation. in R Lee (ed.), Applied Computing and Information Technology. Studies in Computational Intelligence, vol. 695, Springer Verlag, pp. 91-104, 4th International Conference on Applied Computing and Information Technology, ACIT 2016, Las Vegas, United States, 12/12/16. https://doi.org/10.1007/978-3-319-51472-7_7
Akase R, Okada Y. User evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation. In Lee R, editor, Applied Computing and Information Technology. Springer Verlag. 2017. p. 91-104. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-319-51472-7_7
Akase, Ryuya ; Okada, Yoshihiro. / User evaluation prediction models based on conjoint analysis and neural networks for interactive evolutionary computation. Applied Computing and Information Technology. editor / Roger Lee. Springer Verlag, 2017. pp. 91-104 (Studies in Computational Intelligence).
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