Recommender Systems for IoT Enabled m-Health Applications

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.
Originalspracheenglisch
TitelArtificial Intelligence Applications and Innovations
UntertitelAIAI 2018
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten227-237
Seitenumfang11
ISBN (Print)978-3-319-92015-3
DOIs
PublikationsstatusVeröffentlicht - Mai 2018
Veranstaltung2018 IFIP International Conference on Artificial Intelligence Applications and Innovations - Rhodos, Griechenland
Dauer: 25 Mai 201827 Mai 2018

Publikationsreihe

NameIFIP Advances in Information and Communication Technology
Band520

Konferenz

Konferenz2018 IFIP International Conference on Artificial Intelligence Applications and Innovations
KurztitelAIAI 2018
LandGriechenland
OrtRhodos
Zeitraum25/05/1827/05/18

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  • Dieses zitieren

    Polat Erdeniz, S., Maglogiannis, I., Menychtas, A., Felfernig, A., & Tran, T. N. T. (2018). Recommender Systems for IoT Enabled m-Health Applications. in Artificial Intelligence Applications and Innovations: AIAI 2018 (S. 227-237). (IFIP Advances in Information and Communication Technology; Band 520). Cham: Springer. https://doi.org/10.1007/978-3-319-92016-0_21