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.

翻译：社交物联网（SIoT）使互联的智能设备能够共享数据和服务，为个性化服务推荐开辟了机遇。然而，现有研究往往忽略了能增强SIoT环境下推荐准确性和相关性的关键因素。具体而言，现有技术倾向于考虑设备间社交关系的提取，而忽视了服务评论的上下文呈现。本研究旨在通过探索每个设备-服务对的上下文表示来填补这些空白。首先，我们提出一种潜在特征组合技术，通过聚合SIoT内的设备-设备关系来捕捉潜在特征交互。随后，我们利用因子分解机对每个SIoT设备-服务对特有的高阶特征交互进行建模，以实现准确的评分预测。最后，我们提出一种基于评论聚合和特征学习过程的SIoT服务推荐框架。实验评估证明了该框架在提升服务推荐准确性和相关性方面的有效性。