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

Abstract

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.

An Agent-Based Model of Task-Allocation and Resource-Sharing for Social Internet of Things

“…In this paper, we proposed a model that is about the interaction of social objects and their potential to share resources. A recent paper [17] presented a framework that overlays social and fog links; however, the social layer used is a static graph with connection and does not have any dynamics or behavior. Naturally, in the future, we would like to extend the proposed model so that it can be integrated as the social layer in a fog computing environment.…”

“…Specifically, in a fog computing environment, a framework to overlay social and fog links is proposed in a recent paper [17]. We propose a model that only focuses on social objects and their potential ability to act in collaboration.…”

Graph-based service recommendation in Social Internet of Things

“…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. To fuse multiple types of service data and their logical relations for personalized service recommendation methods, Wang et al 31 proposed domain-oriented user and service interaction knowledge graph.…”

A Joint Summarization and Pre-Trained Model for Review-Based Recommendation

“…In addition, users' social relations also contain rich user characteristics. Khelloufi et al [27] took advantage of the social relations between devices to select a suitable service that fits the requirements of the applications and devices, based on the observation that having a given personality type does not necessarily mean that you are compatible with people sharing the same personality type. Ning et al [28] designed a friend recommendation system based on the big-five personality traits model and hybrid filtering, in which the friend recommended process is based on personality traits and users' harmony rating.…”