Bridging taste and health: the role of machine learning in consumer food selection

Abstract Navigating the modern food landscape is complex, often leading consumers to make suboptimal dietary choices due to information, time constraints, and psychological vulnerabilities. Machine learning (ML) presents a promising solution to enhance the contemporary grocery shopping experience by providing personalized dietary recommendations tailored to individual preferences and characteristics. Traditional methods of dietary guidance cannot account for nuanced consumer characteristics and preferences. In contrast, ML algorithms can incorporate specific information about individuals and continuously adapt to provide personalized recommendations, considering factors like historical eating habits, socio-demographics, nutritional requirements, etc. This paper explores the potential value of ML in enhancing personalized dietary recommendations, focusing on personalization at scale and sustained behavior modification. Additionally, it discusses how retailers can integrate ML into the shopping experience to offer customized recommendations based on consumer and inventory data. Future innovations, such as incorporating biometric data, further enhance the precision of personalized recommendations and support ‘Food is Medicine’ programs. By doing so, ML can offer opportunities for retailers to improve customer satisfaction and loyalty by providing personalized recommendations aligned with consumer-centric marketing strategies. By guiding consumer food choices, ML recommendations can potentially improve shopping experiences and benefit consumers and retailers.