IS PhD student Mashfiqui Rabbi will present on "MyBehavior: Automating Personalized Health Feedback Using a Multi-Arm Bandit Model" for this week's Brown Bag Seminar.

MyBehavior: Automating Personalized Health Feedback Using a Multi-Arm Bandit Model

In this work, we propose MyBehavior, a mobile application with a suggestion engine that learns a user’s physical activity and dietary behavior, and provides finely-tuned personalized suggestions. To our knowledge, MyBehavior is the first smartphone app to provide personalized health suggestions automatically, going beyond commonly used one-size-fits-all prescriptive approaches, or tailored interventions from healthcare professionals. MyBehavior uses an online multi-armed bandit model to automatically generate context-sensitive and personalized activity/food suggestions by learning the user’s actual behavior. The app continually adapts its suggestions by exploiting the most frequent healthy behaviors, while sometimes exploring non-frequent behaviors, in order to maximize the user’s chance of reaching a health goal (e.g. weight loss).

We evaluated MyBehavior with a three-week deployment and found that personalized suggestions were more effective and easier to incorporate in users’ daily lives, compared to its generic, prescriptive counterpart.

N.B., This work blends several ideas from machine learning, decision theory, psychological theories of behavior change, and context-aware computing. However, MyBehavior is currently at a very early formative stage, and your feedback is highly valuable for us to take the work forward.