Recommender Systems for IoT Enabled m-Health Applications

Seda Polat Erdeniz, Ilias Maglogiannis, Andreas Menychtas, Alexander Felfernig, Thi Ngoc Trang Tran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recommender systems can help to more easily identify relevant artifacts for users and thus improve user experiences. Currently recommender systems are widely and effectively used in the e-commerce domain (online music services, online bookstores, etc.). On the other hand, due to the rapidly increasing benefits of the emerging topic Internet of Things (IoT), recommender systems have been also integrated to such systems. IoT systems provide essential benefits for human health condition monitoring. In our paper, we propose new recommender systems approaches in IoT enabled mobile health (m-health) applications and show how these can be applied for specific use cases. In this context, we analyze the advantages of proposed recommendation systems in IoT enabled m-health applications.
Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations
Subtitle of host publicationAIAI 2018
Place of PublicationCham
PublisherSpringer
Pages227-237
Number of pages11
ISBN (Print)978-3-319-92015-3
DOIs
Publication statusPublished - May 2018
Event2018 IFIP International Conference on Artificial Intelligence Applications and Innovations - Rhodos, Greece
Duration: 25 May 201827 May 2018

Publication series

NameIFIP Advances in Information and Communication Technology
Volume520

Conference

Conference2018 IFIP International Conference on Artificial Intelligence Applications and Innovations
Abbreviated titleAIAI 2018
CountryGreece
CityRhodos
Period25/05/1827/05/18

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