Intrinsic and Extrinsic Motivation Modeling Essential for Multi-Modal Health Recommender Systems

TL;DR

This paper proposes a novel approach for health recommender systems that models intrinsic and extrinsic motivations from multi-modal data to promote sustainable behavioral health changes.

Contribution

It introduces a motivation modeling framework that distinguishes intrinsic and extrinsic motivations to improve personalized health recommendations.

Findings

Enhanced understanding of motivation types in health behaviors

Framework for integrating multi-modal data into motivation modeling

Potential for improved long-term engagement in health recommendations

Abstract

Managing health lays the core foundation to enabling quality life experiences. Modern computer science research, and especially the field of recommender systems, has enhanced the quality of experiences in fields such as entertainment, shopping, and advertising; yet lags in the health domain. We are developing an approach to leverage multimedia for human health based on motivation modeling and recommendation of actions. Health is primarily a product of our everyday lifestyle actions, yet we have minimal health guidance on making everyday choices. Recommendations are the key to modern content consumption and decisions. Furthermore, long-term engagement with recommender systems is key for true effectiveness. Distinguishing intrinsic and extrinsic motivations from multi-modal data is key to provide recommendations that primarily fuel the intrinsic intentions, while using extrinsic motivation to further support intrinsic motivation. This understanding builds the foundation of sustainable behavioral adaptation for optimal personalized lifestyle health benefits.