Self-adaptive learning implicit user trust behavior method based on depth map convolutional network

Abstract

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.

Description

Technical field [0001] The present invention belongs to the field of deep learning techniques, and more particularly to a method of adaptive learning implicit user trust behavior based on deep map volume network. Background technique [0002] In recent years, with the rapid development of the Internet, the interaction between users and users in the Internet is increasingly close, which makes the social recommendation algorithm based on user social information have received extensive attention, more and more researchers Using users 'trust information, such as users' friends, user concerns, etc., to mitigate data sparse problems, it can alleviate data sparsiness issues in existing recommended algorithms, such as Guo et al., Etc. Trust information synergistic filtering model (TRUSTSVD), etc. However, the classic social recommendation algorithm relies on the quality of the user's trust data. If the user's trust data is very sparse or noise, the performance of the algorithm will decli...

AI Technical Summary

Problems solved by technology

However, the classic social recommendation algorithm is very dependent on the quality of the user's trust data. If the user's trust data is sparse or noisy, the performance of the algorithm will be degraded.

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