Collaborative Recommendation Systems for Allergic Individuals

A recommendation system for food allergies based on collaborative filtering is a valuable tool for providing personalized suggestions to individuals with specific dietary restrictions due to allergies. Its goal is to help users choose foods that match their preferences and nutritional needs while avoiding allergens. This system falls under the broader category of recommendation systems and is used in a variety of sectors, including e-commerce and digital health. This study aims to design a food recommendation system based on collaborative filtering for allergic individuals in order to offer them a more personalized and effective culinary experience that addresses their health issues. To achieve this, we employed several machine learning algorithms, including K-means, fuzzy C-means, MiniBatch K-means, and DBSCAN, to identify the one that best suits our case. The results obtained show that our system provides relevant, personalized recommendations.