  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.
