The Social Internet of Things (SIoT) enables interconnected smart devices to
share data and services, opening up opportunities for personalized service
recommendations. However, existing research often overlooks crucial aspects
that can enhance the accuracy and relevance of recommendations in the SIoT
context. Specifically, existing techniques tend to consider the extraction of
social relationships between devices and neglect the contextual presentation of
service reviews. This study aims to address these gaps by exploring the
contextual representation of each device-service pair. Firstly, we propose a
latent features combination technique that can capture latent feature
interactions, by aggregating the device-device relationships within the SIoT.
Then, we leverage Factorization Machines to model higher-order feature
interactions specific to each SIoT device-service pair to accomplish accurate
rating prediction. Finally, we propose a service recommendation framework for
SIoT based on review aggregation and feature learning processes. The
experimental evaluation demonstrates the framework's effectiveness in improving
service recommendation accuracy and relevance.

Social Internet of Things 的研究，探索服务推荐的上下文表示、潜在特征组合技术以及服务推荐框架对提高准确性和相关性的有效性进行实验评估。

社交物联网的上下文感知服务推荐系统

Context-Aware Service Recommendation System for the Social Internet of  Things

Social Internet of Things (SIoT), a promising and emerging paradigm that
injects the notion of social networking into smart objects (i.e., things),
paving the way for the next generation of Internet of Things. However, due to
the risks and uncertainty, a crucial and urgent problem to be settled is
establishing reliable relationships within SIoT, that is, trust evaluation.
Graph neural networks for trust evaluation typically adopt a straightforward
way such as one-hot or node2vec to comprehend node characteristics, which
ignores the valuable semantic knowledge attached to nodes. Moreover, the
underlying structure of SIoT is usually complex, including both the
heterogeneous graph structure and pairwise trust relationships, which renders
hard to preserve the properties of SIoT trust during information propagation.
To address these aforementioned problems, we propose a novel knowledge-enhanced
graph neural network (KGTrust) for better trust evaluation in SIoT.
Specifically, we first extract useful knowledge from users' comment behaviors
and external structured triples related to object descriptions, in order to
gain a deeper insight into the semantics of users and objects. Furthermore, we
introduce a discriminative convolutional layer that utilizes heterogeneous
graph structure, node semantics, and augmented trust relationships to learn
node embeddings from the perspective of a user as a trustor or a trustee,
effectively capturing multi-aspect properties of SIoT trust during information
propagation. Finally, a trust prediction layer is developed to estimate the
trust relationships between pairwise nodes. Extensive experiments on three
public datasets illustrate the superior performance of KGTrust over
state-of-the-art methods.

本文提出了一种知识增强的图神经网络（KGTrust），用于在社会物联网（SIoT）中进行更好的信任评估，该方法结合了用户和对象的语义知识以及增强的信任关系，能够有效地捕捉 SIoT 信任的多方面特性。实验结果表明，KGTrust 方法优于当前的最先进方法。