receteame.com: A Persuasive Social Recommendation System

Palanca, Javier; Heras, Stella; Botti, Vicente; Julián, Vicente

doi:10.1007/978-3-319-07551-8_40

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In (Palanca et al, 2014), we presented receteame.com, a persuasive social recommendation system whose goal is to recommend the most appropriated recipe to each specific user, taking into account their preferences, food restrictions and social context. In this work, we propose an improvement of the system to automatically detect allergens in recipes, based on their ingredients composition, and to prevent the system from recommending inappropriate recipes to people with specific food restrictions.…”

Section: Motivationmentioning

confidence: 99%

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

Alemany-Bordera

Barberá

Pérez-López

et al. 2016

ADCAIJ

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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/

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Section: Motivationmentioning

confidence: 99%

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

Alemany-Bordera

Barberá

Pérez-López

et al. 2016

ADCAIJ

Self Cite

View full text Add to dashboard Cite

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“…In particular, recommender technology can be integrated into current recipe websites and apps to improve support for users who wish to adopt healthier and/or more sustainable eating habits. A disadvantage of such personalized systems is that they typically reinforce existing eating habits ( Starke, 2019 ), encouraging users to buy more of the same products rather than try healthier alternatives, and even so-called “persuasive” agent-based recommenders may still be based on existing lifestyle choices and social network activity ( Palanca et al, 2014 ). NLP-based methods not only make it easier to compute the healthiness or sustainability of recipes but could also allow the design of personalized interventions that are rapidly explainable, updatable, and deployable, highlighting different categories that cater to different eating goals, such as health or sustainability.…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

Erp¹,

Reynolds²,

Maynard³

et al. 2021

Front. Artif. Intell.

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In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

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An Agent-Based Application for Automatic Classification of Food Allergies and Intolerances in Recipes

Alemany

Heras

Palanca

et al. 2016

Advances in Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection

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receteame.com: A Persuasive Social Recommendation System

Cited by 5 publications

References 9 publications

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

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

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

An Agent-Based Application for Automatic Classification of Food Allergies and Intolerances in Recipes

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