The Social Internet of Things (SIoT) refers to the interconnection of Internet of Things (IoT) devices with the addition of a social layer, enabling them to mimic humanlike behavior. This technology facet facilitates data and service sharing among devices, creating a scalable network of socially interconnected devices. However, this rapid increase in device numbers and generated data has led to the challenge of data explosion. To address this challenge, service recommendation systems play a crucial role in improving network navigability and service discovery within the SIoT context. However, existing research often overlooks important aspects that could make service selection more pertinent and relevant. Specifically, current service recommendation approaches focus on extracting social relationships between devices while neglecting the contextual presentation of service reviews. In this research, we introduce a service recommendation framework for SIoT context that integrates service-review aggregation and feature learning processes by utilizing the Factorization Machines to capture complex feature interactions unique to each SIoT device-service pair. Empirical results across three benchmark datasets demonstrate that the proposed CASR-SIoT framework consistently outperforms stateof-the-art baselines, achieving up to 14% improvement in Recall, 8% in Precision, and 15% reduction in RMSE, thereby validating its effectiveness in delivering accurate and context-aware service recommendations in SIoT environments.