Personalized Recipe Recommendations with Food2Vec: An AI Approach

The increasing trend of cooking at home, accelerated by events like the COVID-19 pandemic, has led to a surge in demand for accessible and personalized recipe recommendations. While numerous platforms offer recipe sharing and video content, a gap exists in services that tailor recommendations to individual user preferences. This article explores a novel approach using Food2Vec to bridge this gap, offering a personalized recipe recommendation service.

Existing AI research related to cooking primarily focuses on food image classification, ingredient prediction, and nutritional analysis. While some studies explore recipe similarity based on ingredients or text data, few address the challenge of personalizing recipe recommendations by quantitatively analyzing user preferences. This paper builds upon the Food2Vec technology to incorporate user information and provide a more tailored experience.

Our proposed service leverages the Food2Vec technique to analyze the relationships between food items and recipes. By embedding both into a shared vector space, we can quantify their similarity. Furthermore, we integrate user-specific data, such as dietary restrictions, preferred cuisines, and skill level, to personalize the recommendations. This dual approach ensures that the suggested recipes are both relevant and appealing to the individual user.

The developed service can recommend from a database of approximately 1300 recipes. The recommendations are based on a combination of Food2Vec similarity scores and user preference matching. The system presents users with a ranked list of recipes, along with explanations of why each recipe was recommended, fostering trust and transparency.

This paper introduces a personalized recipe recommendation service powered by Food2Vec. By considering both food relationships and user preferences, the service offers a significant improvement over generic recipe platforms. We anticipate that this technology will play a crucial role in the future of non-face-to-face recipe recommendations, enhancing the cooking experience for home chefs.

Food2Vec, inspired by Doc2Vec, is a technique that represents food items as vectors in an embedding space. This allows for the quantification of similarity between different foods based on their ingredients, preparation methods, and other relevant characteristics. By applying Doc2Vec principles to food data, Food2Vec enables the identification of subtle relationships that might not be apparent through traditional methods.

The power of Food2Vec lies in its ability to capture the complex relationships between food items. By combining this with user preference data, the recommendation engine can identify recipes that align with the user's taste profile. For example, if a user enjoys spicy foods and has a preference for Asian cuisine, the system can leverage Food2Vec to find recipes that incorporate these elements.

The personalized recipe recommendation service based on Food2Vec has the potential for numerous future applications. It can be integrated into smart kitchen appliances, used to generate customized meal plans, and even assist in grocery shopping. We expect that this technology will continue to evolve, becoming an indispensable tool for home cooks seeking inspiration and guidance.