Bargaining agents based system for automatic classification of potential allergens in recipes

José ALEMANY, Stella HERAS, Javier PALANCA, Vicente JULIÁN

Abstract


The automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users’ attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations.

Keywords


recommendation system; food allergy; multi-agent system

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References


Adomavicius, G. and Tuzhilin, A., 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734–749.

Elsweiler, D. and Harvey, M., 2015. Towards automatic meal plan recommendations for balanced nutrition. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 313–316. ACM.

Freyne, J. and Berkovsky, S., 2010. Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces, pages 321–324. ACM. https://doi.org/10.1145/1719970.1720021

Hammond, K. J., 1989. Case-based Planning: Viewing Planning As a Memory Task. Academic Press Professional, Inc., San Diego, CA, USA. ISBN 0-12-322060-2. https://doi.org/10.1016/b978-0-12-322060-8.50018-8

Harvey, M., Ludwig, B., and Elsweiler, D., 2013. You are what you eat: Learning user tastes for rating prediction. In String Processing and Information Retrieval, pages 153–164. Springer. https://doi.org/10.1007/978-3-319-02432-5_19

Kolodner, J. L., 1987. Capitalizing on failure through case-based inference. Technical report, DTIC Document.

Mazzotta, I., De Rosis, F., and Carofiglio, V., 2007. Portia: A user-adapted persuasion system in the healthy-eating domain. Intelligent Systems, IEEE, 22(6):42–51. https://doi.org/10.1109/MIS.2007.115

Palanca, J., Heras, S., Botti, V., and Julian, V., 2014. receteame.com: a Persuasive Social Recommendation System. In 12th International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer. https://doi.org/10.1007/978-3-319-07551-8_40

Phanich, M., Pholkul, P., and Phimoltares, S., 2010. Food recommendation system using clustering analysis for diabetic patients. In Information Science and Applications (ICISA), 2010 International Conference on, pages 1–8. IEEE. https://doi.org/10.1109/icisa.2010.5480416

Schall, D., 2015. Social Network-Based Recommender Systems.

Teng, C.-Y., Lin, Y.-R., and Adamic, L. A., 2012. Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference, pages 298–307. ACM. https://doi.org/10.1145/2380718.2380757

Ueda, M., Takahata, M., and Nakajima, S., 2011. User's food preference extraction for personalized cooking recipe recommendation. Semantic Personalized Information Management: Retrieval and Recommendation SPIM 2011, page 98.

Van der Aalst, W. M. and Song, M., 2004. Mining Social Networks: Uncovering interaction patterns in business processes. In Business Process Management, pages 244–260. Springer.

Zhou, X., Xu, Y., Li, Y., Josang, A., and Cox, C., 2012. The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review, 37(2):119–132. https://doi.org/10.1007/s10462-011-9222-1




DOI: http://dx.doi.org/10.14201/ADCAIJ2016524351





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