Towards interactive explanation-based nutrition virtual coaching systems

Berk Buzcu*, Melissa Tessa, Igor Tchappi, Amro Najjar, Joris Hulstijn, Davide Calvaresi, Reyhan Aydoğan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article number5
Number of pages26
JournalAutonomous Agents and Multi-Agent Systems
Volume38
Issue number1
DOIs
Publication statusPublished - Jun 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Funding

Open access funding provided by University of Applied Sciences and Arts Western Switzerland (HES-SO). This work has been supported by the CHIST-ERAgrant CHIST-ERA-19-XAI-005, and by the Swiss National Science Foundation (G.A. 20CH21_195530), the Italian Ministry for Universities and Research, the Luxembourg National Research Fund (G.A. INTER/CHIST/19/14589586), the Scientific and Research Council of Turkey (TUBITAK, G.A. 120N680).

FundersFunder number
University of Applied Sciences and Arts Western Switzerland (HES-SO)
CHIST-ERAgrantCHIST-ERA-19-XAI-005
Swiss National Science FoundationG.A. 20CH21_195530
Italian Ministry for Universities and Research
Luxembourg National Research FundG.A. INTER/CHIST/19/14589586
Scientific and Research Council of Turkey (TUBITAK)G.A. 120N680

    Keywords

    • Explainable AI
    • Interactive
    • Nutrition virtual coach
    • Recommender systems

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