Abstract
The automatic recommendation of recipes for users with some kind of food allergies or intolerances is still a complex and open problem. One of the limitations is the lack of databases that labels ingredients of recipes with their associated allergens. This limitation may cause the recommendation of inappropriate recipes to people with specific food restrictions. In order to try to solve this, this paper proposes a collaborative multi-agent system that automatically detects food allergies in nutrients and labels ingredients with their potential allergens. 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.
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Notes
- 1.
WAO World Allergy Organization, Food allergy statistics: http://www.worldallergy.org/public/allergic_diseases_center/foodallergy/.
- 2.
USDA National Nutrient Database for Standard Reference: http://ndb.nal.usda.gov/.
- 3.
Asociación Española de Personas con Alergia a Alimentos y Látex: http://www.aepnaa.org.
- 4.
- 5.
- 6.
Food Allergy Research & Education: http://www.foodallergy.org.
- 7.
We only show in the table a subset of all machine learning techniques that we tested.
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Acknowledgements
This work was supported by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politècnica de València.
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Alemany, J., Heras, S., Palanca, J., Julián, V. (2016). An Agent-Based Application for Automatic Classification of Food Allergies and Intolerances in Recipes. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M. (eds) Advances in Practical Applications of Scalable Multi-agent Systems. The PAAMS Collection. PAAMS 2016. Lecture Notes in Computer Science(), vol 9662. Springer, Cham. https://doi.org/10.1007/978-3-319-39324-7_1
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