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This study proposes and evaluates a novel approach utilizing ensemble machine learning techniques for personalized meal services to address a critical gap in understanding AI-powered decision-making within the food delivery and restaurant industry. We draw inspiration from diverse fields, including non-traditional simulation methodologies and open innovation dynamics, to create a framework that leverages the combined strengths of individual algorithms. Three machine learning algorithms – decision trees, logistic regression, and naïve Bayes – are rigorously evaluated for their efficacy in classifying and assigning algorithms within an ensemble model for a new service. A simulated dataset, informed by expert tagging, is the training ground, ensuring practical relevance. We employ the voting probability metric on a held-out test set to provide a robust measure of accuracy in this critical task. Our findings reveal the significant potential of AI-powered personalized meal services. Ensemble models demonstrate high accuracy, showcasing the collaboration of combining individual algorithms. This originality lies in applying ensemble techniques to a business case with far-reaching implications for management and societal well-being. Beyond technical success, we explore this technology's broader impact. AI-powered food recommendations can enhance accessibility for individuals with dietary needs, promote healthier lifestyles through nutritious meal suggestions, and generate new job opportunities. Acknowledging limitations and future research avenues, we invite further exploration of diverse machine learning algorithms and broader applications across various domains.
Yaiprasert et al. (Thu,) studied this question.