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The emergence of Artificial Intelligence of Things (AIoT) has provided novelinsights for many social computing applications such as group recommendersystems. As distance among people has been greatly shortened, it has been amore general demand to provide personalized services to groups instead ofindividuals. In order to capture group-level preference features fromindividuals, existing methods were mostly established via aggregation and facetwo aspects of challenges: secure data management workflow is absent, andimplicit preference feedbacks is ignored. To tackle current difficulties, thispaper proposes secure Artificial Intelligence of Things for implicit GroupRecommendations (SAIoT-GR). As for hardware module, a secure IoT structure isdeveloped as the bottom support platform. As for software module, collaborativeBayesian network model and non-cooperative game are can be introduced asalgorithms. Such a secure AIoT architecture is able to maximize the advantagesof the two modules. In addition, a large number of experiments are carried outto evaluate the performance of the SAIoT-GR in terms of efficiency androbustness.

Secure Artificial Intelligence of Things for Implicit Group Recommendations