Back to Search

Towards interactive explanation-based nutrition virtual coaching systems

Title	Towards interactive explanation-based nutrition virtual coaching systems
Author	Buzcu, Berk, Tessa, M., Tchappi, I., Najjar, A., Hulstijn, J., Calvaresi, D., Aydoğan, Reyhan
Publication Date:	2024-01
Publication Place	- Springer
Subject	Explainable AI, Interactive, Nutrition virtual coach, Recommender systems
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1387-2532
Record ID	1c1b8299-ae0d-45cd-a38c-477dcbe1dd5b
Library Location	Computer Science
Date	2024-01
Notes	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
Sample Text	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

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

Towards interactive explanation-based nutrition virtual coaching systems

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

Publication Date 2024-01

Publication Place - Springer

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

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1387-2532

Record ID 1c1b8299-ae0d-45cd-a38c-477dcbe1dd5b

Library Location Computer Science

Date 2024-01

Notes 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

Sample Text 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