Towards interactive explanation-based nutrition virtual coaching systems
Berk Buzcu (Özyeğin University, University of Applied Sciences and Arts Western Switzerland)
Melissa Tessa (High National School of Computer Science ESI ex-INI, Algiers)
Igor Tchappi (Université du Luxembourg)
Amro Najjar (Luxembourg Institute of Science and Technology, Université du Luxembourg)
Joris Hulstijn (Université du Luxembourg)
Davide Calvaresi (University of Applied Sciences and Arts Western Switzerland)
Reyhan Aydoğan (Universidad de Alcalá, Özyeğin University, TU Delft - Interactive Intelligence)
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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.
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