Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.
With the popularization of the internet of things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. However, the problems of poor performance in a highly noisy environment and less consideration of calculation resources are urgent to be resolved in a practical RFF application domain. The authors propose a new RFF identification method based on metric learning. They used power spectrum density (PSD) to extract the RFF from the nonlinearity of the RF front end. Then they adopted the large margin nearest neighbor (LMNN) classification algorithm to identify eight software-defined radio (SDR) devices. Different from existing RFF identification algorithms, the proposed LMNN method is more general and can learn the optimal metric from the wireless communication environment. Furthermore, they propose a new training and test strategy based on mixed SNR, which significantly improves the performance of conventional low-complexity RFF identification methods. Experimental results show that the proposed method can achieve 99.8% identification accuracy with 30dB SNR and 96.83% with 10dB SNR. In conclusion, the study demonstrates the effectiveness of the proposed method in recognition efficiency and computational complexity.
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