Towards interactive explanation-based nutrition virtual coaching systems

Name: Towards interactive explanation-based nutrition virtual coaching systems
Author: Buzcu, Berk, Tessa, M., Tchappi, I., Najjar, A., Hulstijn, J., Calvaresi, D., Aydoğan, Reyhan

İsim	Towards interactive explanation-based nutrition virtual coaching systems
Yazar	Buzcu, Berk, Tessa, M., Tchappi, I., Najjar, A., Hulstijn, J., Calvaresi, D., Aydoğan, Reyhan
Basım Tarihi:	2024-01
Basım Yeri	- Springer
Konu	Explainable AI, Interactive, Nutrition virtual coach, Recommender systems
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1387-2532
Kayıt Numarası	1c1b8299-ae0d-45cd-a38c-477dcbe1dd5b
Lokasyon	Computer Science
Tarih	2024-01
Notlar	University of Applied Sciences and Arts Western Switzerland (HES-SO) ; CHIST-ERAgrant ; Swiss National Science Foundation (SNSF) ; Ministry of Education, Universities and Research (MIUR) ; Luxembourg National Research Fund ; TÜBİTAK
Örnek Metin	The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.
DOI	10.1007/s10458-023-09634-5
Cilt	38

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Towards interactive explanation-based nutrition virtual coaching systems

Yazar Buzcu, Berk, Tessa, M., Tchappi, I., Najjar, A., Hulstijn, J., Calvaresi, D., Aydoğan, Reyhan

Basım Tarihi 2024-01

Basım Yeri - Springer

Konu Explainable AI, Interactive, Nutrition virtual coach, Recommender systems

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1387-2532

Kayıt Numarası 1c1b8299-ae0d-45cd-a38c-477dcbe1dd5b

Lokasyon Computer Science

Tarih 2024-01

Notlar University of Applied Sciences and Arts Western Switzerland (HES-SO) ; CHIST-ERAgrant ; Swiss National Science Foundation (SNSF) ; Ministry of Education, Universities and Research (MIUR) ; Luxembourg National Research Fund ; TÜBİTAK

Örnek Metin The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.

DOI 10.1007/s10458-023-09634-5

Cilt 38