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

Institut für Softwaretechnologie (7160)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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.

Originalsprache	englisch
Titel	Artificial Intelligence Applications and Innovations
Untertitel	AIAI 2018
Erscheinungsort	Cham
Herausgeber (Verlag)	Springer
Seiten	227-237
Seitenumfang	11
ISBN (Print)	978-3-319-92015-3
DOIs	https://doi.org/10.1007/978-3-319-92016-0_21
Publikationsstatus	Veröffentlicht - Mai 2018
Veranstaltung	2018 IFIP International Conference on Artificial Intelligence Applications and Innovations - Rhodos, Griechenland Dauer: 25 Mai 2018 → 27 Mai 2018

Publikationsreihe

Name	IFIP Advances in Information and Communication Technology
Band	520

Konferenz

Konferenz	2018 IFIP International Conference on Artificial Intelligence Applications and Innovations
Kurztitel	AIAI 2018
Land/Gebiet	Griechenland
Ort	Rhodos
Zeitraum	25/05/18 → 27/05/18

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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. S. 227-237 (IFIP Advances in Information and Communication Technology; Band 520).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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, Bd. 520, Springer, Cham, S. 227-237, 2018 IFIP International Conference on Artificial Intelligence Applications and Innovations, Rhodos, Griechenland, 25/05/18. https://doi.org/10.1007/978-3-319-92016-0_21

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title = "Recommender Systems for IoT Enabled m-Health Applications",

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.",

author = "{Polat Erdeniz}, Seda and Ilias Maglogiannis and Andreas Menychtas and Alexander Felfernig and Tran, {Thi Ngoc Trang}",

year = "2018",

month = may,

doi = "10.1007/978-3-319-92016-0_21",

language = "English",

isbn = "978-3-319-92015-3",

series = "IFIP Advances in Information and Communication Technology",

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AU - Tran, Thi Ngoc Trang

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N2 - 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.

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.

U2 - 10.1007/978-3-319-92016-0_21

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

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ER -

Recommender Systems for IoT Enabled m-Health Applications

Abstract

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