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

arXiv: https://arxiv.org/abs/2401.15188 · HTML: https://mlsanity.com/paper/2401.15188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits](https://mlsanity.com/paper/1507.04523/) (2015)
- [Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation](https://mlsanity.com/paper/2008.09472/) (2020)
- [Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging](https://mlsanity.com/paper/1901.00449/) (2018)
- [Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2009.06108/) (2020)
- [Contextual Bandit with Missing Rewards](https://mlsanity.com/paper/2007.06368/) (2020)
- [A Survey on Mobile Affective Computing](https://mlsanity.com/paper/1410.1648/) (2014)
- [BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System](https://mlsanity.com/paper/2106.10898/) (2021)

## Built on by (most cited first, 2 of 2)

- [Contextual bandits with entropy-based human feedback](https://mlsanity.com/paper/2502.08759/) (2025)
- [Predicting and Understanding College Student Mental Health with Interpretable Machine Learning](https://mlsanity.com/paper/2503.08002/) (2025)

## Abstract (arXiv)

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.