Context-Aware Service Recommendation System for the Social Internet of Things
This work addresses service recommendation challenges for interconnected smart devices, but it appears incremental by building on existing methods like Factorization Machines.
The paper tackles the problem of inaccurate service recommendations in the Social Internet of Things by proposing a framework that incorporates contextual representations and latent feature interactions, resulting in improved accuracy and relevance as demonstrated experimentally.
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.