CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health

Author:

Sheng Yu, Narjes Nourzad, Randye J. Semple, Yixue Zhao, Emily Zhou, Bhaskar Krishnamachari

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI)

journal:

date:

2024-01-26 00:00:00

Abstract

The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand beyond traditional clinical settings to support daily life. However, the effectiveness of current mHealth solutions is impeded by the lack of context-awareness, personalization, and modularity to foster their reusability. This paper introduces CAREForMe, a contextual multi-armed bandit (CMAB) recommendation framework for mental health. Designed with context-awareness, personalization, and modularity at its core, CAREForMe harnesses mobile sensing and integrates online learning algorithms with user clustering capability to deliver timely, personalized recommendations. With its modular design, CAREForMe serves as both a customizable recommendation framework to guide future research, and a collaborative platform to facilitate interdisciplinary contributions in mHealth research. We showcase CAREForMe's versatility through its implementation across various platforms (e.g., Discord, Telegram) and its customization to diverse recommendation features.

PDF: CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health.pdf