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

Abstract

Recommendation system; Food allergy; Multi-agent systemThe 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. MotivationWhen the doctor diagnoses a patient with an allergy, the set of substances that produce extreme sensitivity in the patient's immune system should be avoided. In the case of food allergies, the difficulty is greater because avoiding a food is not an easy task when many of them are composed by others (e.g. mayonnaise, is composed of oil, egg, vinegar, etc.). This is an awkward situation of high impact because it involves nutrition, a necessary and daily task in the lives of people activity. This increasingly affects to more individuals in our society (up to 8% in children and 2% in adults) 1 . Moreover, the problem of food allergies is not resolved by simply avoiding certain foods, because the lack of nutrients they provide must be compensated with other foods.People affected by food allergies are forced to become expert nutritionists to maintain a healthy life, free from allergens that they cannot tolerate. Currently, Internet is the most popular way of obtaining information about allergies. On the Internet, for instance, the World Allergy Organization (WAO) regulates and offers the terminology used to characterize allergies information. However, the information is difficult to understand because of its complexity and quantity. Thus, traditional search and navigation activities are being combined or even replaced by direct interactions between users in the form of recommendations, advice and warnings; 2 out of 3 take into account the recommendations of other users to make decisions (about products, treatments, entertainment, etc.); and of these, 69% gives a lot or some credibility to what their friends or acquaintances say on social networks. 1 WAO World Allergy Organization, Food allergy statistics: http://www.worldallergy.org/public/allergic_diseases_center/foodallergy/

Understanding the Cooking Process with English Recipe Text

“…There has been a growing interest in the analysis of recipes by scholars in NLP and deep learning, for example, studying of recipes from a linguistic perspective (Jurafsky, 2014), detecting allergens in recipes for users to improve recipe recommendation systems (Alemany et al, 2016), finding patterns of spice and food combinations in recipes (Jain et al, 2015), exploring food and recipes to help meet the challenges of sustainable and healthy eating (Willett et al, 2019) and making a robot that can cook (Bollini et al, 2013).…”