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Conference Papers Year : 2018

Recommender Systems for IoT Enabled m-Health Applications

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Seda Polat Erdeniz
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  • PersonId : 1033606
Ilias Maglogiannis
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  • PersonId : 991053
Andreas Menychtas
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Alexander Felfernig
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Thi Tran
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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.
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hal-01821320 , version 1 (22-06-2018)


Attribution - CC BY 4.0



Seda Polat Erdeniz, Ilias Maglogiannis, Andreas Menychtas, Alexander Felfernig, Thi Tran. Recommender Systems for IoT Enabled m-Health Applications. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.227-237, ⟨10.1007/978-3-319-92016-0_21⟩. ⟨hal-01821320⟩
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