A Food Recommender System in Academic Environments Based on Machine
  Learning Models
Abolfazl Ajami Babak Teimourpour
Background: People's health depends on the use of proper diet as an important
factor. Today, with the increasing mechanization of people's lives, proper
eating habits and behaviors are neglected. On the other hand, food
recommendations in the field of health have also tried to deal with this issue.
But with the introduction of the Western nutrition style and the advancement of
Western chemical medicine, many issues have emerged in the field of disease
treatment and nutrition. Recent advances in technology and the use of
artificial intelligence methods in information systems have led to the creation
of recommender systems in order to improve people's health. Methods: A hybrid
recommender system including, collaborative filtering, content-based, and
knowledge-based models was used. Machine learning models such as Decision Tree,
k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field
of food recommender systems on 2519 students in the nutrition management system
of a university. Student information including profile information for basal
metabolic rate, student reservation records, and selected diet type is received
online. Among the 15 features collected and after consulting nutrition experts,
the most effective features are selected through feature engineering. Using
machine learning models based on energy indicators and food selection history
by students, food from the university menu is recommended to students. Results:
The AdaBoost model has the highest performance in terms of accuracy with a rate
of 73.70 percent. Conclusion: Considering the importance of diet in people's
health, recommender systems are effective in obtaining useful information from
a huge amount of data. Keywords: Recommender system, Food behavior and habits,
Machine learning, Classification