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

Abstract

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.

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

“…Smart sensors and wearables paved the path for dramatically improving the accuracy and efficiency of recommendation systems related to the e-health domain. In [80], Nag et al coupled two layers of user-context modeling (exogenous and endogenous layers) to develop a recommendation system for a nutrition guidance system that controls sodium intake. The exogenous context defines the situation outside the body, which can be acquired using monitoring systems such as (weather stations, pollution monitors, traffic data, etc.).…”