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People care more about the solutions to their problems rather than dataalone. Inherently, this means using data to generate a list of recommendationsfor a given situation. The rapid growth of multi-modal wearables and sensorshave not made this jump effectively in the domain of health. Modern usercontent consumption and decision making in both cyber (e.g. entertainment,news) and physical (eg. food, shopping) spaces rely heavily on targetedpersonalized recommender systems. The utility function is the primary rankingmethod to predict what a given person would explicitly prefer. In this work wedescribe two unique layers of user and context modeling that can be coupled totraditional recommender system approaches. The exogenous layer incorporatesfactors outside of the person's body (eg. location, weather, social context),while the endogenous layer integrates data to estimate the physiologic orinnate needs of the user. This is accomplished through multi-modal sensor dataintegration applied to domain-specific utility functions, filters andre-ranking weights. We showcase this concept through a nutrition guidancesystem focused on controlling sodium intake at a personalized level,dramatically improving upon the fixed recommendations.

Endogenous and Exogenous Multi-Modal Layers in Context Aware Recommendation Systems for Health