Food Recommender System in Sub-Saharan Africa: Challenges and Prospects

Auteurs

Pagou E.S., Kamla V.C., Tchappi I., Ngathic J., Tsakam L., Najjar A.

Référence

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 566 LNICST, pp. 276-287, 2024

Description

Nutrition is one of the most important lifestyle components that can be altered, therefore even minor changes or bad food choices can have a huge impact on health. Food recommendation systems are especially useful for maintaining a balanced diet or preventing chronic diseases such as diabetes, cancer, and cardiovascular disease, which are responsible for 63% of deaths worldwide, according to the World Health Organization. The underlying problem with building a meal suggestion system is a lack of contextual information for creating a user profile. It is critical to consider the social, cultural, economic, political, and environmental facts relevant to certain regions of the world. Creating user profiles based on Artificial Intelligence (AI), on the other hand, need contextual information that takes into account social, cultural, economic, political, and environmental elements. In Sub-Saharan Africa, where diet is connected to ethnicity and climatic seasons, AI-based dietary advice can target healthy or malnourished individuals. This article examines the current status of food recommendation systems, obstacles, lessons learned, and future directions and open questions. The objective is to provide additional user and meal profile characteristics to future item and recipe suggestion systems, hence encouraging healthy eating for Sub-Saharan Africa.

Lien

doi:10.1007/978-3-031-56396-6_17