Towards automated IoT service recommendation

Noirie, Ludovic; Pallec, Michel Le; Ammar, Nesrine

doi:10.1109/icin.2017.7899397

Cited by 13 publications

(4 citation statements)

References 5 publications

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“…(Details are mentioned in subsection 4.3.7). Also, the author in [95] exploited graph techniques to build an RS that provides IoT services recommendations to the user based on their own IoT devices. The system combines three main steps: IoT services modeling, IoT service catalog (classification algorithm), and service matching algorithms (Recommendations).…”

Section: Recommendations With Graph Database Modelmentioning

confidence: 99%

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan

Yao

Wang

et al. 2021

The Computer Journal

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Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

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Section: Recommendations With Graph Database Modelmentioning

confidence: 99%

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan

Yao

Wang

et al. 2021

The Computer Journal

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“…Matsui and Choi (2017) proposed an RS that suggests indoor comfort products by exploiting HEMS (Home Energy Management System) data like indoor humidity, temperature, luminance, power consumption, and clothing quantity. Generally, people are not aware of the services related to the smart objects they purchased, so an RS can facilitate by recommending IoT services to grasp the connected smart objects (Noirie et al, 2017). Mashal et al (2020) develop a recommendation system using a decision-making approach based on hybrid multi-criteria to suggest the most suitable IoT application.…”

Section: Some Other Iot Recommender Systemsmentioning

confidence: 99%

An Era of Recommendation Technologies in IoT: Categorisation by techniques, Challenges and Future Scope

Ahlawat¹,

Rana²

2021

JST

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The evolution of the Internet of Things (IoT) accelerates the augmentation of data present on the Internet and possibilities for connections to the more dynamic and heterogeneous devices to the Internet. Recommendation technologies have proven their capabilities of digging the personalised information by proactive filtering in many application domains and can also be a backbone platform in IoT for identifying personalised things, services and relevant artefacts by prevailing over information overload problems. This paper is a comprehensive literature review that categorises IoT recommender systems by exploring the literature’s different IoT based recommendation techniques. We conclude the paper by discussing the challenges and future scope for IoT based recommendations techniques to advancing and widening the frontiers of this research area.

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“…Another study applied a characterization algorithm to build an IoT service catalog which is updated each time a user experiences a new service. Authors developed a graphical user interface for their IoT service recommender system [38].…”

Section: Previous Work On Iot Recommender Systemsmentioning

confidence: 99%

Deep AutoEncoders in Recommender Systems: An Application about Internet of Things Service Recommendation

Birim

2021

Bilişim Teknolojileri Dergisi

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Özet-Nesnelerin İnterneti (IoT) küçük ve birbirine bağlı, internet aracılığıyla bilgi paylaşımı sağlayan cihazlardan oluşan bir sistemdir. Gelecek IoT cihazlarının gerçekleştirdiği hizmetlerin sayısının oldukça artmasını beklemektedir. Bu nedenle IoT hizmeti önerme, gelecekte çok önemli bir faaliyet halini alacaktır. Bu çalışma, IoT hizmetleri tavsiyesi için en iyi yöntemi bulmayı hedeflemektedir. Bu amaç doğrultusunda bu çalışma derin otokodlayıcılar yöntemi ile, kullanıcılara IoT hizmet ve uygulamalarını kullandıkları cihazlara bağlı olarak önermeyi amaçlamıştır. Derin otokodlayıcılar, kullanıcı hizmet tercih matrisini tahmin edebilmek için sinir ağlarını kullanır. Bu çalışmada kullanılan veri, gerçek bir Sosyal IoT veri setinden yararlanılarak oluşturulmuştur. Sonuçlara göre derin otokodlayıcılar, teknoloji harikası öneri yöntemlere göre daha başarılı bir performans ortaya koymuştur. Bulunan sonuçlara göre Derin otokodlayıcılar diğer yöntemlere göre performans göstergelerinde %13.5 ile %69.5 arasında değişen oranlarda iyileştirme sağlamıştır. Bu sonuca ek olarak ELU (exponential linear units), özel bir çeşit aktivasyon fonksiyonu ile oto kodlayıcıların performansının arttırılabileceği görülmüştür. Bu çalışma IoT hizmet önerisi literatürüne geleneksel ve teknoloji harikası kabul edilen yöntemlere bir alternatif sunarak katkı sağlamaktadır.

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Towards automated IoT service recommendation

Cited by 13 publications

References 5 publications

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

An Era of Recommendation Technologies in IoT: Categorisation by techniques, Challenges and Future Scope

Deep AutoEncoders in Recommender Systems: An Application about Internet of Things Service Recommendation

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