Workshop on Health Recommender Systems

Recommendations are becoming ever more important in health settings with the aim being to assist people live healthier lives. Three previous workshops on Health Recommender Systems (HRS) have incorporated diverse research fields and problems in which recommender systems can improve our awareness, understanding and behaviour regarding our own, and the general public’s health. At the same time, these application areas bring new challenges into the recommender community. Recommendations that influence the health status of a patient need to be legally sound and, as such, today, they often involve a human in the loop to make sure the recommendations are appropriate. To make the recommender infallible, complex domain-specific user models need to be created, which creates privacy issues. While trust in a recommendation needs to be explicitly earned through, for example, transparency, explanations and empowerment, other systems might want to persuade users into taking beneficial actions that would not be willingly chosen otherwise. Multiple and diverse stakeholders in health systems produce further challenges. Taking the patient’s perspective, simple interaction and safety against harmful recommendations might be the prioritized concern. For clinicians and experts, on the other hand, what matters is precise and accurate content. Healthcare and insurance providers and clinics all have other priorities. This workshop will deepen the discussions started at the three prior workshops and will work towards further development of the research topics in Health Recommender Systems.

Organizers
  • David Elsweiler, University of Regensburg, Germany
  • Bernd Ludwig, University of Regensburg, Germany
  • Alan Said, University of Gothenburg, Sweden
  • Hanna Schäfer, University of Konstanz, Germany
  • Helma Torkamaan, University of Duisburg-Essen, Germany
  • Christoph Trattner, University of Bergen, Norway
Website

https://healthrecsys.github.io/2019/

Date

Friday, Sept 20, 2019, 09:00-17:30

Location

Room 104+105

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