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

Ryuya Akase, Yoshihiro Okada

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトルApplied Computing and Information Technology
編集者Roger Lee
出版社Springer Verlag
ページ91-104
ページ数14
ISBN(印刷版)9783319514710
DOI
出版ステータス出版済み - 2017
イベント4th International Conference on Applied Computing and Information Technology, ACIT 2016 - Las Vegas, 米国
継続期間: 12 12 201612 14 2016

出版物シリーズ

名前Studies in Computational Intelligence
695
ISSN(印刷版)1860-949X

その他

その他4th International Conference on Applied Computing and Information Technology, ACIT 2016
国/地域米国
City Las Vegas
Period12/12/1612/14/16

All Science Journal Classification (ASJC) codes

  • 人工知能

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