Towards Argumentation-based Recommendations for Personalised Patient Empowerment

Juan Manuel Fernandez, Marco Mamei, Stefano Mariani, Felip Miralles, Alexander Steblin, Eloisa Vargiu, Franco Zambonelli

David Elsweiler, Santiago Hors-Fraile, Bernd Ludwig, Alan Said, Hanna Schäfer, Christoph Trattner, Helma Torkamaan, André Calero Valdez (eds.)
Proceedings of the 2nd International Workshop on Health Recommender Systems co-located with the 11th International Conference on Recommender Systems (RecSys 2017), pages 2  5
CEUR Workshop Proceedings
August 2017

Patient empowerment is a key issue in healthcare. Approaches to increase patient empowerment encompass patient self-management programs. In this paper we present ArgoRec, a recommender system that exploits argumentation for leveraging explanatory power and natural language interactions so as to improve patients’ user experience and quality of recommendations. ArgoRec is part of a great effort concerned with supporting complex chronic patients in, for instance, their daily life activities after hospitalisation, pursued within the CONNECARE project by following a co-design approach to define a comprehensive Self-Management System.

Tags:

Publication

— authors

Juan Manuel Fernandez, Marco Mamei, Stefano Mariani, Felip Miralles, Alexander Steblin, Eloisa Vargiu, Franco Zambonelli

— editors

David Elsweiler, Santiago Hors-Fraile, Bernd Ludwig, Alan Said, Hanna Schäfer, Christoph Trattner, Helma Torkamaan, André Calero Valdez

— status

published

— sort

paper in proceedings

— publication date

August 2017

— volume

Proceedings of the 2nd International Workshop on Health Recommender Systems co-located with the 11th International Conference on Recommender Systems (RecSys 2017)

— volume

1953

— pages

2  5

— venue

Como, Italy

— location

Como, Italy

— organization

ACM

URLs

original page  |  original PDF

identifiers

— DOI

10.1145/3109859.3109955

— ACM

https://dl.acm.org/citation.cfm?doid=3109859.3109955

— DBLP

http://dblp.org/rec/conf/recsys/ElsweilerHLSSTT17

— print ISSN

1613-0073

— print ISBN

978-1-4503-4652-8

notes

— note

Co-located with the 11th ACM Conference on Recommender Systems

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