Symbolic knowledge extraction for explainable nutritional recommenders

Name: Symbolic knowledge extraction for explainable nutritional recommenders
Author: Magnini, M., Ciatto, G., Cantürk, Furkan, Aydoğan, Reyhan, Omicini, A.

İsim	Symbolic knowledge extraction for explainable nutritional recommenders
Yazar	Magnini, M., Ciatto, G., Cantürk, Furkan, Aydoğan, Reyhan, Omicini, A.
Basım Tarihi:	2023-06
Basım Yeri	- Elsevier
Konu	Explainable artificial intelligence, Neural networks, Nutrition, Recommendation systems, Symbolic knowledge extraction
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0169-2607
Kayıt Numarası	e4a45132-08db-422c-9986-6c2f67486d7d
Lokasyon	Computer Science
Tarih	2023-06
Notlar	CHIST-ERA IV project ; Ministry of Education, Universities and Research (MIUR) ; TÜBİTAK
Örnek Metin	Background and objective: 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 designing these 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 RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. Conclusions 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.
DOI	10.1016/j.cmpb.2023.107536
Cilt	235

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Symbolic knowledge extraction for explainable nutritional recommenders

Yazar Magnini, M., Ciatto, G., Cantürk, Furkan, Aydoğan, Reyhan, Omicini, A.

Basım Tarihi 2023-06

Basım Yeri - Elsevier

Konu Explainable artificial intelligence, Neural networks, Nutrition, Recommendation systems, Symbolic knowledge extraction

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0169-2607

Kayıt Numarası e4a45132-08db-422c-9986-6c2f67486d7d

Lokasyon Computer Science

Tarih 2023-06

Notlar CHIST-ERA IV project ; Ministry of Education, Universities and Research (MIUR) ; TÜBİTAK

Örnek Metin Background and objective: 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 designing these 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 RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. Conclusions 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.

DOI 10.1016/j.cmpb.2023.107536

Cilt 235