Secure Artificial Intelligence of Things for Implicit Group Recommendations

Keping Yu, Zhiwei Guo, Yu Shen, Wei Wang, Jerry Chun Wei Lin, Takuro Sato

研究成果: Article査読

1 被引用数 (Scopus)

抄録

The emergence of Artificial Intelligence of 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 has been more general demand for the provision of personalized services aimed at groups instead of individuals. The existing methods for capturing group-level preference features from individuals have mostly been established via aggregation and face two challenges: secure data management workflows are absent, and implicit preference feedback is ignored. To tackle these current difficulties, this paper proposes secure AIoT for implicit group recommendations (SAIoT-GR). For the hardware module, a secure IoT structure is developed as the bottom support platform. For the software module, a collaborative Bayesian network model and noncooperative game are introduced as algorithms. This 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 SAIoT-GR in terms of efficiency and robustness.

本文言語English
ジャーナルIEEE Internet of Things Journal
DOI
出版ステータスAccepted/In press - 2021

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

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