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

arXiv: https://arxiv.org/abs/2308.08499 · HTML: https://mlsanity.com/paper/2308.08499/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [IoT-Enabled Social Relationships Meet Artificial Social Intelligence](https://mlsanity.com/paper/2103.01776/) (2021)
- [A Survey of Hybrid Human-Artificial Intelligence for Social Computing](https://mlsanity.com/paper/2103.15558/) (2021)
- [Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings](https://mlsanity.com/paper/2204.06988/) (2022)
- [A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things](https://mlsanity.com/paper/2306.01163/) (2023)

## Built on by (most cited first, 2 of 2)

- [Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems](https://mlsanity.com/paper/2402.16904/) (2024)
- [Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations](https://mlsanity.com/paper/2402.16052/) (2024)

## Abstract (arXiv)

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.