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.
Due to the complex space environment, spacecraft telemetry signals are accompanied by a large amount of noise, and the accuracy of fault diagnosis is low by directly using the original telemetry signals. This paper proposes a fault diagnosis method for spacecraft control systems based on principal component analysis (PCA) and residual network (ResNet). Firstly, grayscale images are generated by denoising the telemetry signal of the spacecraft control system through PCA; Secondly, the images are input into the residual network to extract deep-level features; Finally, the Softmax classifier is used for classification to realize the fault diagnosis of the spacecraft control system. The research results show that the diagnostic accuracy of the method proposed in this paper reaches 95.33%, which is higher than other diagnostic models, and the method can be used for the actual fault classification of spacecraft control systems.
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