Health recommender systems

Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations (to appear)

Recommender Systems use implicit and explicit user feedback to recommend desired products or items online. When the recommendation item is a task or behavior change activity, several variables, such as the difficulty of the task and users’ ability to …

STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data

Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful stress models have been proposed in the literature, there is still a missing connection between user engagement …

Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System

Integrating behavior change theories and persuasive design principals into health recommender systems.

Towards a User Integration Framework for Personal HealthDecision Support and Recommender Systems

A multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components:Empower, Encourage, and Engage.

Mobile Mood Tracking: An Investigation of Concise and Adaptive Measurement Instruments

Investigation of a short and adaptive mood measurement using smartphones that addresses both assessment quality and app quality

PAX

A health recommender system app for mental health promotion

Rating-Based Preference Elicitation for Recommendation of Stress Intervention

This paper develops a better understanding of the preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation, and offers a design solution as a functional feedback model for mobile health applications.

Multi-Criteria Rating-Based Preference Elicitation in Health Recommender Systems

In this paper, we investigate the criteria for the rating of a health promotion recommendation using an online survey. Drawing on both the relevant literature and the users’ responses, we came up with a list of 33 criteria that users consider when they rate a health promotion recommendation.