Secure Artificial Intelligence of Things for Implicit Group Recommendations
This work addresses secure and implicit group recommendations for social computing applications in AIoT, representing an incremental improvement over existing aggregation-based methods.
The paper tackles the problem of providing personalized group recommendations in AIoT by addressing challenges in secure data management and implicit preferences, proposing SAIoT-GR with a secure IoT structure and algorithms like collaborative Bayesian networks and non-cooperative games, and reports experimental results on efficiency and robustness.
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.