The author of the text is a data scientist from New York who worked on a project using Neo4j and R to develop a food recommendation engine that aimed to improve people's health. The project was inspired by the growing obesity rate in the United States, with a focus on understanding the connection between food consumption and health outcomes. The dataset used was from the National Health and Nutrition Examination Survey conducted by the US Center for Disease Control and Prevention in 2012. The author developed a graph data model using Neo4j that included nodes for people, eating events, foods, and characteristics, allowing for easy exploration of data and generation of high-quality recommendations. The recommendation engine considered three key components: accessibility, nutrition, and pleasure, with a focus on personalization and customization to cater to individual needs. The author demonstrated the effectiveness of the system by identifying individuals who might benefit from healthier eating habits and providing personalized lunch recommendations that met their nutritional and pleasure requirements. The project highlights the potential of Neo4j in developing data-driven solutions for health promotion and nutrition, with opportunities for further refinement and expansion using additional data points and feedback loops.