Symbolic Knowledge Extraction for Explainable Nutritional Recommenders


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Matteo Magnini, Giovanni Ciatto, Furkan Canturk, Reyhan Aydoǧan, Andrea Omicini

Computer Methods and Programs in Biomedicine

This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designingthese kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional recommendation systems, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. Thanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective—while the whole process of recommendation generation is made explainable.

(keywords) explainable artificial intelligence, symbolic knowledge extraction, recommendation systems, nutrition, neural networks

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Matteo Magnini, Giovanni Ciatto, Furkan Canturk, Reyhan Aydoǧan, Andrea Omicini

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submitted

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Computer Methods and Programs in Biomedicine

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32

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