Service Recommendation for User Groups in Internet of Things Environments Using Member Organization-Based Group Similarity Measures

Lee, Jin-Seo; Ko, In-Young

doi:10.1109/icws.2016.43

Cited by 15 publications

(18 citation statements)

References 13 publications

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“…For QoS-aware service recommendation for IoT Mashup applications, Cao et al 28 captured the relationships among mashup, services, and their links with relational topic model and exploited factorization machines to train the latent topics for recommending services. Since user groups' preferences for IoT-based services differ significantly from individual users, Lee et al 29 proposed a service recommendation using userbased collaborative filtering, which determines neighbor user groups by considering several member organization-based group similarity metrics such as the group size-based, common member-based, and member preference-based metrics. Khelloufi et al 30 proposed a service recommendation system by considering the social relationships between device owners, as incorporating user's social features can enable contextaware recommendation and increase the efficiency of the recommended services.…”

Section: Related Workmentioning

confidence: 99%

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Graph-based service recommendation in Social Internet of Things

Chen

Tao

Zheng

et al. 2021

International Journal of Distributed Sensor Networks

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While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

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Section: Related Workmentioning

confidence: 99%

“…To our best knowledge, only few studies [27][28][29] addressed the problem of service recommendation in SIoT with considering object's social relationships. For example, Saleem et al 27 utilized people-objects relationship and objects-objects relationship in SIoT to recommend services among various IoT applications.…”

Section: Related Workmentioning

confidence: 99%

Graph-based service recommendation in Social Internet of Things

Chen

Tao

Zheng

et al. 2021

International Journal of Distributed Sensor Networks

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“…Another study offered user-based collaborative filtering with a group similarity algorithm that takes into account the membership in a group [15]. They calculated the similarity between user groups with the variables of group size, common members, and member preferences.…”

Section: Previous Work On Iot Recommender Systemsmentioning

confidence: 99%

“…However, when the service IoT literature is investigated there are a few studies that offer recommendation systems for the IoT services. Few studies in the literature used tripartite graph-based model [13], artificial neural network (ANN) [14], user-based collaborative filtering [15], and a user profile similaritybased model [16]. Still, IoT services need a recommender system which offers new services to the users that best fits their interests [17].…”

Section: Introductionmentioning

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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“…We adopt two commonly used metrics to measure the performance of the GR-HTM algorithm, including precision [38] and recall [39]:…”

Section: Evaluation Criteria and Objectsmentioning

confidence: 99%

Group recommendation based on hybrid trust metric

2020

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Group recommendation is a special service type which has the ability to satisfy a group's common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness. ARTICLE HISTORY

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Service Recommendation for User Groups in Internet of Things Environments Using Member Organization-Based Group Similarity Measures

Cited by 15 publications

References 13 publications

Graph-based service recommendation in Social Internet of Things

Graph-based service recommendation in Social Internet of Things

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

Group recommendation based on hybrid trust metric

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