Secure Artificial Intelligence of Things for Implicit Group Recommendations

The emergence of Artificial Intelligence Things (AIoT) has provided novel insights for many social computing applications, such as group recommender systems. As the distances between people have been greatly shortened, there more general demand provision personalized services aimed at groups instead individuals. existing methods capturing group-level preference features from individuals mostly established via aggregation and face two challenges: 1) secure data management workflows are absent 2) implicit feedback is ignored. To tackle these current difficulties, this article proposes AIoT recommendations (SAIoT-GRs). For hardware module, a Internet structure developed bottom support platform. software collaborative Bayesian network model noncooperative game introduced algorithms. This architecture able to maximize advantages modules. In addition, large number experiments carried out evaluate performance SAIoT-GR in terms efficiency robustness.