In The Journal of nutrition ; h5-index 61.0
BACKGROUND : Along with the popularity of smartphones, artificial intelligence-based personalized suggestions can be seen as promising ways to change eating habits toward more desirable diets.
OBJECTIVES : Two issues raised by such technologies were addressed in this study. The first hypothesis tested is a recommender system based on automatically learning simple association rules between dishes of the same meal that would make it possible to identify plausible substitutions for the consumer. The second hypothesis tested is that for an identical set of dietary-swaps suggestions, the more the user is-or thinks to be-involved in the process of identifying the suggestion, the higher is their probability of accepting the suggestion.
METHODS : Three studies are presented in this article, first, we present the principles of an algorithm to mine plausible substitutions from a large food consumption database. Second, we evaluate the plausibility of these automatically mined suggestions through the results of online tests conducted for a group of 255 adult participants. Afterward, we investigated the persuasiveness of 3 suggestion methods of such recommendations in a population of 27 healthy adult volunteers through a custom designed smartphone application.
RESULTS : The results firstly indicated that a method based on automatic learning of substitution rules between foods performed relatively well identifying plausible swaps suggestions. Regarding the form that should be used to suggest, we found that when users are involved in selecting the most appropriate recommendation for them, the resulting suggestions were more accepted (OR = 3.168; P < 0.0004).
CONCLUSIONS : This work indicates that food recommendation algorithms can gain efficiency by taking into account the consumption context and user engagement in the recommendation process. Further research is warranted to identify nutritionally relevant suggestions.
Vandeputte Jules, Herold Pierrick, Kuslii Mykyt, Viappiani Paolo, Muller Laurent, Martin Christine, Davidenko Olga, Delaere Fabien, Manfredotti Cristina, Cornuéjols Antoine, Darcel Nicolas
2023-Feb
artificial intelligence, behavior change, decision sciences, food recommendation algorithms, healthy diets
