Self-adaptive learning implicit user trust behavior method based on depth map convolutional network
An adaptive learning and convolutional network technology, applied in the field of adaptive learning implicit user trust behavior, can solve problems such as algorithm performance degradation, and achieve the effect of improving recommendation performance and strengthening connections
Pending Publication Date: 2022-01-07
HANGZHOU DIANZI UNIV
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The invention belongs to the technical field of deep learning, and discloses a self-adaptive learning implicit user trust behavior method based on a depth map convolutional network. The method comprises the following steps: 1, trust information preprocessing: filtering unreliable trust information by using historical behaviors of a user to relieve a trust noise problem; 2, graph convolutional network learning: a depth graph convolutional network performs learning according to score data and trust features of the user, and outputs features of the user; 3, adaptive learning of implicit trust features: learning reliable user information by using an adaptive learning matrix, and speculating trust information and trust features of the user. According to the method, unreliable trust features can be filtered out, and meanwhile, the self-adaptive matrix learns the behavior features of the user, so that the implicit trust behavior logic of the user is learned, the trust features of the user are deduced and perfected, the relation between score data of the user and trust data of the user is enhanced, and the recommendation performance is improved.
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