Recommender Systems for IoT Enabled m-Health Applications

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

doi:10.1007/978-3-319-92016-0_21

Recommender Systems for IoT Enabled m-Health Applications

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

Institute of Software Engineering and Artificial Intelligence (7160)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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 language	English
Title of host publication	Artificial Intelligence Applications and Innovations
Subtitle of host publication	AIAI 2018
Place of Publication	Cham
Publisher	Springer
Pages	227-237
Number of pages	11
ISBN (Print)	978-3-319-92015-3
DOIs	https://doi.org/10.1007/978-3-319-92016-0_21
Publication status	Published - May 2018
Event	2018 IFIP International Conference on Artificial Intelligence Applications and Innovations - Rhodos, Greece Duration: 25 May 2018 → 27 May 2018

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	520

Conference

Conference	2018 IFIP International Conference on Artificial Intelligence Applications and Innovations
Abbreviated title	AIAI 2018
Country/Territory	Greece
City	Rhodos
Period	25/05/18 → 27/05/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-319-92016-0_21

Cite this

Recommender Systems for IoT Enabled m-Health Applications. / Polat Erdeniz, Seda; Maglogiannis, Ilias; Menychtas, Andreas et al.
Artificial Intelligence Applications and Innovations: AIAI 2018. Cham: Springer, 2018. p. 227-237 (IFIP Advances in Information and Communication Technology; Vol. 520).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Polat Erdeniz, S, Maglogiannis, I, Menychtas, A, Felfernig, A & Tran, TNT 2018, Recommender Systems for IoT Enabled m-Health Applications. in Artificial Intelligence Applications and Innovations: AIAI 2018. IFIP Advances in Information and Communication Technology, vol. 520, Springer, Cham, pp. 227-237, 2018 IFIP International Conference on Artificial Intelligence Applications and Innovations, Rhodos, Greece, 25/05/18. https://doi.org/10.1007/978-3-319-92016-0_21

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AB - 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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ER -

Recommender Systems for IoT Enabled m-Health Applications

Abstract

Publication series

Conference

UN SDGs

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