Healthy Personalized Recipe Recommendations for Weekly Meal Planning

Abstract

Nowadays, in the pursuit of personalized health and well-being, dietary choices are critical. This paper introduces a novel recommendation system designed to provide users with personalized meal plans, consisting of breakfast, lunch, snack, and dinner, in alignment with their health history and preferences from other similar users. More specifically, our system exploits collaborative filtering first to identify other users with similar dietary preferences and uses this information to propose suitable recipes to individuals. The whole process is enhanced by analyzing the individual’s health history, including dietary restrictions, nutritional needs, and specific diet plans, such as low-carb or vegetarian. This ensures that the generated meal plans are not only aligned with the user’s taste but also contribute to the overall wellness of the user. A distinctive feature of our system is its dynamic adaptation feature, which enables users to make real-time adjustments to their meal plans based on their personal constraints and preferences, directly impacting future recommendations. We evaluate the usability of the system through a series of experiments on a large real-world data set of recipes, showing that our system is able to provide highly personalized, dynamic, and accurate recommendations.

Keywords

• collaborative filtering
• recipes
• meal plan personalization
• content based
• machine learning
• recommendation systems