You are What You Eat! Tracking Health Through Recipe Interactions

On today’s World Wide Web, social recommender systems have become a commodity regardless of application domain. Even tangible items such as food and clothes have become social. Together with a seemingly endless amount of personalization and recommender systems ranging from movies, music, or consumer products, recipe recommender systems are attracting many users looking for inspiration on the next thing to purchase or cook. There is however a conceptual difference between recommending consumer goods for leisure and entertainment, and recommending food. What people eat has a direct effect on their health, an aspect commonly overlooked in the context of recommendation. In this work, we present an early analysis of users’ interactions with recipes (ratings) on the online social network Allrecipes.com. We compare the interaction patterns of users from locations known to have poor health to users from locations known to have good health in order to identify whether there is an observable difference between the two populations. Our results point to a statistically significant difference between the healthy and unhealthy groups, a difference that could potentially be used to create health-conscious, personalized, recommendation services to aid people in their daily lives.