Endogenous and Exogenous Multi-Modal Layers in Context Aware Recommendation Systems for Health
This work addresses personalized health recommendations for users of wearables, though it appears incremental as it builds upon traditional recommender system approaches.
The authors tackled the problem of ineffective multi-modal wearable data utilization in health recommendation systems by proposing endogenous and exogenous context modeling layers, which when applied to a nutrition guidance system for sodium control achieved dramatic improvements over fixed recommendations.
People care more about the solutions to their problems rather than data alone. Inherently, this means using data to generate a list of recommendations for a given situation. The rapid growth of multi-modal wearables and sensors have not made this jump effectively in the domain of health. Modern user content consumption and decision making in both cyber (e.g. entertainment, news) and physical (eg. food, shopping) spaces rely heavily on targeted personalized recommender systems. The utility function is the primary ranking method to predict what a given person would explicitly prefer. In this work we describe two unique layers of user and context modeling that can be coupled to traditional recommender system approaches. The exogenous layer incorporates factors outside of the person's body (eg. location, weather, social context), while the endogenous layer integrates data to estimate the physiologic or innate needs of the user. This is accomplished through multi-modal sensor data integration applied to domain-specific utility functions, filters and re-ranking weights. We showcase this concept through a nutrition guidance system focused on controlling sodium intake at a personalized level, dramatically improving upon the fixed recommendations.