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Deep Distributed Learning-based POI Recommendation Under Mobile Edge Networks
IEEE Internet of Things Journal
  • Zhiwei Guo, Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China
  • Keping Yu, School of Computer and Information Engineering, Bengbu University, Bengbu, China
  • Neeraj Kumar, Department of Computer Science and Engineering, Thapar Institute of Engineering, India
  • Wei Wei, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
  • Shahid Mumtaz, Nottingham Trent University, UK
  • Mohsen Guizani, Mohamed bin Zayed University of Artificial Intelligence
Document Type

With the rapid development of edge intelligence in wireless communication networks, mobile edge networks (MEN) have been broadly discussed in academia. Supported by considerable geographical data acquisition ability of mobile Internet of Things (IoT), the MEN can also provide spatial locations-based social service to users. Therefore, suggesting reasonable points-of-interest (POIs) to users is essential to improve user experience of MEN. As the simple user-location data is usually sparse and not informative, existing literature attempted to extend feature space from two perspectives: contextual patterns and semantic patterns. However, previous approaches mainly focused on internal features of users, yet ignoring latent external features among them. To address this challenge, in this paper, a deep distributed learning-based POI recommendation (Deep-PR) method is proposed for situations of MEN. In particular, hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes. Besides, propagation operations are embedded to iteratively reoptimize expressions of the feature space. The successive effect of the above two aspects contributes a lot to more fine-grained feature spaces, so that recommendation accuracy can be ensured. Two types of experiments are also carried out on three real-world datasets to assess both efficiency and stability of the proposed Deep-PR. Compared with seven typical baselines with respect to four evaluation metrics, obtained results of the overall performance of the Deep-PR are excellent. IEEE

Publication Date
  • Computer aided instruction,
  • Convolutional neural networks,
  • Deep distributed learning,
  • deep information fusion,
  • Distance learning,
  • Electronic mail,
  • Feature extraction,
  • Internet of Things,
  • mobile edge networks,
  • point-of-interest,
  • Semantics

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Citation Information
Z. Guo, K. Yu, N. Kumar, W. Wei, S. Mumtaz and M. Guizani, "Deep Distributed Learning-based POI Recommendation Under Mobile Edge Networks," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3202628.