# Secure Artificial Intelligence of Things for Implicit Group Recommendations

arXiv: https://arxiv.org/abs/2104.11699 · HTML: https://mlsanity.com/paper/2104.11699/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users](https://mlsanity.com/paper/2005.12979/) (2020)

## Built on by (most cited first, 4 of 4)

- [FedNILM: Applying Federated Learning to NILM Applications at the Edge](https://mlsanity.com/paper/2106.07751/) (2021)
- [GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss](https://mlsanity.com/paper/2304.00242/) (2023)
- [Mulberry Leaf Yield Prediction Using Machine Learning Techniques](https://mlsanity.com/paper/2110.01394/) (2021)
- [A Multiple Criteria Decision Analysis based Approach to Remove Uncertainty in SMP Models](https://mlsanity.com/paper/2209.15260/) (2022)

## Abstract (arXiv)

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications such as group recommender systems. As distance among people has been greatly shortened, it has been a more general demand to provide personalized services to groups instead of individuals. In order to capture group-level preference features from individuals, existing methods were mostly established via aggregation and face two aspects of challenges: secure data management workflow is absent, and implicit preference feedbacks is ignored. To tackle current difficulties, this paper proposes secure Artificial Intelligence of Things for implicit Group Recommendations (SAIoT-GR). As for hardware module, a secure IoT structure is developed as the bottom support platform. As for software module, collaborative Bayesian network model and non-cooperative game are can be introduced as algorithms. Such a secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of the SAIoT-GR in terms of efficiency and robustness.