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Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes
Jules Vandeputte1, Pierrick Herold2, Mykyt Kuslii2
1Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA Paris-Saclay, Palaiseau, France.
The Journal of Nutrition
|March 9, 2023
Summary
Artificial intelligence can personalize diet suggestions for healthier eating. User involvement in choosing food swaps significantly increases acceptance of AI-driven recommendations.
Area of Science:
- Artificial Intelligence
- Nutritional Science
- Human-Computer Interaction
Background:
- Smartphones and AI offer personalized suggestions to improve eating habits.
- AI-driven dietary recommendations are a promising approach for public health.
Purpose of the Study:
- To develop and evaluate an AI recommender system for identifying plausible food substitutions.
- To assess the impact of user involvement on the acceptance of dietary change suggestions.
Main Methods:
- An algorithm was developed to mine food substitutions from consumption data.
- Online tests with 255 adults evaluated the plausibility of AI-generated food swaps.
- A smartphone app study with 27 adults examined the persuasiveness of different suggestion methods.
Main Results:
- The AI-based food substitution algorithm effectively identified plausible dietary swaps.
- User engagement in selecting recommendations led to significantly higher acceptance rates (OR = 3.168; P < 0.0004).
Conclusions:
- Food recommendation algorithms are more effective when considering consumption context and user engagement.
- Further research is needed to ensure AI suggestions are nutritionally beneficial.
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