Artificial Intelligence based Food Nutrients Recommendation System using Enhanced Customer Behavior Learning Approaches

Digital marketing has made significant steps in leveraging advanced technologies such as machine learning to provide personalized recommendations to food and nutritional product companies. Artificial Intelligence (AI), a branch of computer science aimed at imitating thought processes, learning abilities and knowledge management, is increasingly used in experimental nutrients recommendation. The food and nutrition product industry, using data science techniques to recommend products based on specific needs and preferences. Previous method analysis the kids’ food product analysis failed in feature analysis based on product buying consumer behavior in digital marketing. To overcome further problems, a new method called Spectral Spider Optimization with Fuzzy Neural Network (SSO-FNN) with Support Vectored Neural Networks ($\mathrm{SVN}^{2}$) to advertise the best food items using maximum threshold values for selecting the best features is proposed. The $\mathrm{SVN}^{2}$ is prediction result based on recommendation food, accuracy, precision, recall, f1-score, and error rate and time complexity. These metrics uses confusion matrix evaluation and its outperforms the previous approaches.