Healthy Eating Habits Support System Considering User Taste Preferences and Nutritional Balance

Yuta Hayashi; Ryouta Oku; Hiroshi Takenouchi; Masataka Tokumaru

doi:10.20965/jaciii.2018.p0097

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JACIII Vol.22 No.1 pp. 97-103

doi: 10.20965/jaciii.2018.p0097

(2018)

Paper:

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Healthy Eating Habits Support System Considering User Taste Preferences and Nutritional Balance

Yuta Hayashi^, Ryouta Oku^, Hiroshi Takenouchi^, and Masataka Tokumaru^*

^*Kansai University Graduate School
3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan

^**Fukuoka Institute of Technology
3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan

^***Kansai University
3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan

Received:

November 2, 2016

Accepted:

October 16, 2017

Published:

January 20, 2018

Keywords:

eating habits support, user taste preferences, nutritional balance, interactive evolutionary computation

Abstract

We propose a Healthy Eating Habits Support System (HEHSS) which considers user taste preferences and nutritional balance. The proposed system comprises a Nutritional Management System (NMS) and a Kansei Retrieval System (KRS). The NMS generates nutritionally balanced menus using the tabu search method. The KRS learns user taste preferences through interaction with a user, and then uses this information to recommend appropriate menus for that user from those generated by the NMS. Consequently, the HEHSS recommends menus that consider nutritional balance and match the user’s taste preferences. Simulation results demonstrate that the HEHSS recommended menus that satisfied nutritional needs and learned a user’s taste preferences with greater than 80% accuracy when we focused on liked and disliked tastes after continuous use for a long period (approximately 2 months).

Cite this article as:

Y. Hayashi, R. Oku, H. Takenouchi, and M. Tokumaru, “Healthy Eating Habits Support System Considering User Taste Preferences and Nutritional Balance,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 97-103, 2018.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] COOKPAD, http://cookpad.com [accessed Oct. 20, 2016]

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[3] [3] Q. Li, W. Chen, and L. Yu, “Distributed Cooking Recipe Recommendation and Adaptation,” J. of Software, Vol.8, No.3, pp. 528-537, 2013.

[4] [4] T. Ueta, M. Iwakami, and T. Ito, “A Recipe Recommendation System Based on Automatic Nutrition Information Extraction,” Int. Conf. on Knowledge Science, Engineering and Management, pp. 79-90, Springer, 2011.

[5] [5] Y. Tokumi, J. Hakamata, and M. Tokumaru, “Development of a Nutritional Management System for a Healthy Eating Habits Support System,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 324-334, 2013.

[6] [6] C. Nishikawa, A. Nagai, T. Ito, and S. Maruyama, “An Implementation of a Menu-List Recommendation System Providing Feedback from User,” Contemporary Challenges and Solutions in Applied Artificial Intelligence, pp. 55-60, Springer, 2013.

[7] [7] F. Glover, “Tabu Search-Part I,” ORSA J. on computing, Vol.1, No.3, pp. 190-206, 1989.

[8] [8] F. Glover, “Tabu Search-Part II,” ORSA J. on computing, Vol.2, No.1, pp. 4-32, 1990.

[9] [9] L. M. o. Health and W. i. Japan, “The Dietary Reference Intakes for Japanese – 2010 –” (in Japanese), http://www.mhlw.go.jp/shingi/2009/05/s0529-4.html [accessed Oct. 20, 2016]

[10] [10] R. M. Kling and P. Banerjee, “ESP: A New Standard Cell Placement Package Using Simulated Evolution,” Proc. of the 24th ACM/IEEE Design Automation Conf., pp. 60-66, ACM, 1987.

[11] [11] J. R. Smith, “Designing Biomorphs with an Interactive Genetic Algorithm,” ICGA, pp. 535-538, 1991.

[12] [12] Calorepi, http://calorepi.com [accessed Oct. 20, 2016]

Healthy Eating Habits Support System Considering User Taste Preferences and Nutritional Balance

Yuta Hayashi*, Ryouta Oku*, Hiroshi Takenouchi**, and Masataka Tokumaru***

Yuta Hayashi^, Ryouta Oku^, Hiroshi Takenouchi^, and Masataka Tokumaru^*