Graph-based service recommendation in Social Internet of Things

Chen, Yuanyi; Tao, Yanyun; Zheng, Zengwei; Chen, Dan

doi:10.1177/15501477211009047

Cited by 9 publications

(7 citation statements)

References 33 publications

(54 reference statements)

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“…As shown in Figure 5, a request from the user or a specific application is sent to the service Request Controller (RC), where the controller assigns an ID to each request it receives and keeps them as a list of service requests to prevent duplicate requests, and when an object receives a request, it checks the background of the service to remove duplicate requests. 3,33 Because each association is based on a specific type of service, the controller has information about each association and its type of service, which is provided as follows:…”

Section: Single Service Requestmentioning

confidence: 99%

Community detection and service discovery on Social Internet of Things

Rad

Rahmani

Sahafi

et al. 2023

Int J Communication

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Summary Due to the increasing growth of objects and problems such as increased traffic, overload, delay in response, and low search volume in the service discovery process in the complex Social Internet of Things (SIoT) environment, we provide an effective mechanism in the service discovery process by grouping objects based on common criteria that help us improve service search performance. In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities.

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Section: Single Service Requestmentioning

confidence: 99%

Community detection and service discovery on Social Internet of Things

Rad

Rahmani

Sahafi

et al. 2023

Int J Communication

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“…The framework presented in [16] adopts a graph-based approach, considering both the social relationships among heterogeneous objects in the SIoT and user preferences. User preferences are learned from object usage events using a latent variable model, while the relationships between users, objects, and services are modeled using a knowledge graph.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [16] presented a graph-based service recommendation framework for addressing service recommendation challenges in the context of SIoT. The framework utilizes a knowledge graph representation, where users and SIoT objects are treated as entities.…”

Section: Related Workmentioning

confidence: 99%

“…The works in [16] presented a graph-based service recommendation framework was proposed by jointly considering social relationships between heterogeneous objects in SIoT and user's preferences. The User's preferences were learned from their object usage events with a latent variable model while users, objects and their relationships were modeled using a knowledge graph.…”

Section: Introductionmentioning

confidence: 99%

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A Social-Relationships-Based Service Recommendation System for SIoT Devices

Khelloufi

Ning

Dhelim

et al. 2021

IEEE Internet Things J.

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The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices' SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item-item structures underlying the sparse multi-modal contents in SIoT environment. In this paper, we propose a latent-based SIoT recommendation system that learns item-item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-theart SIoT recommendation methods and validated its efficacy at mining latent relationships from multi-modal features.

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“…The SIOT concept has recently attracted considerable attention. For example, Nitti et al [27] focused on how to deal with the information provided by SIOT members, such that they could independently establish a relationship network, while Chen et al [28] regarded SIOT as a knowledge graph to understand user preferences through potential variables. Son et al [29] have proposed a trust-aware recommendation system suitable for SIOT.…”

Section: Introductionmentioning

confidence: 99%

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

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In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.

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Graph-based service recommendation in Social Internet of Things

Cited by 9 publications

References 33 publications

Community detection and service discovery on Social Internet of Things

Community detection and service discovery on Social Internet of Things

A Social-Relationships-Based Service Recommendation System for SIoT Devices

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

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