Automated recommender systems have been
developed to make up for human inadequacies in decision
making while solving the information overload problem.
They have also found profound use in the area of diet and
nutrition. Nevertheless, child nutrition in recommender
systems is yet under researched, with very few works
found in this area. This research work employs a
switching hybrid recommendation technique that is a
combination of user-based collaborative filtering and
human expert knowledge for both healthy and
malnourished children; to cater for the nutrition needs of
children on a large and much improved scale while being
accessible and available to children, parents and caregivers in different locations at the same time. Six
elementary schools in Nigeria were visited for data
gathering on children food interests, likes and dislikes.
Open ended and dichotomous questions were used to
obtain vital information for the system; and these
responses are incorporated as initial user and food
database to check the cold-start problem. Waterlows’
classification model was used to profile and classify users
into their health classes and user-based collaborative
filtering algorithm was used to recommend meals to the
users based on user-user similarity. Human expert
knowledge built from interaction with nutritionist was
incorporated into the system and used in the
recommendation process for both healthy and
malnourished children. System evaluation results show
the overall optimal performance and acceptance of the
system. The results of this work can be adopted to reduce
the scourge of malnutrition in children through healthy
diet provisioning, especially in the Nigerian context.
Malnutrition; Diet Recommendation; School Aged Children; Allergy; Nutrition.