  Online healthcare communities provide users with various healthcare
interventions to promote healthy behavior and improve adherence. When faced
with too many intervention choices, however, individuals may find it difficult
to decide which option to take, especially when they lack the experience or
knowledge to evaluate different options. The choice overload issue may
negatively affect users' engagement in health management. In this study, we
take a design-science perspective to propose a recommendation framework that
helps users to select healthcare interventions. Taking into account that users'
health behaviors can be highly dynamic and diverse, we propose a multi-armed
bandit (MAB)-driven recommendation framework, which enables us to adaptively
learn users' preference variations while promoting recommendation diversity in
the meantime. To better adapt an MAB to the healthcare context, we synthesize
two innovative model components based on prominent health theories. The first
component is a deep-learning-based feature engineering procedure, which is
designed to learn crucial recommendation contexts in regard to users'
sequential health histories, health-management experiences, preferences, and
intrinsic attributes of healthcare interventions. The second component is a
diversity constraint, which structurally diversifies recommendations in
different dimensions to provide users with well-rounded support. We apply our
approach to an online weight management context and evaluate it rigorously
through a series of experiments. Our results demonstrate that each of the
design components is effective and that our recommendation design outperforms a
wide range of state-of-the-art recommendation systems. Our study contributes to
the research on the application of business intelligence and has implications
for multiple stakeholders, including online healthcare platforms, policymakers,
and users.
