  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.
