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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However, the classic social recommendation algorithm is very dependent on the quality of the user's tru

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

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[0046] In order to better understand the object, structure, and function of the present invention, a method of further detailed understanding of adaptive learning implicit user trust behavior based on depth map splitting networks will be described in connection with the accompanying drawings.

[0047] like figure 1As shown, an adaptive learning implicit user trust behavior method based on deep map volume network is to let depth neural networks to learn user trust behavior by filtering out unreliable trust information, reacting user implicit Behavioral logic. The model is divided into three parts. The first part is to filter out unreliable trust information by using the user's historical behavior to alleviate the noise problem of trust; the second part is the map volume learning section, depth map volume network The user's scoring data and the trust feature are learned, and the user's feature is output. The third part is to learn reliable user information using an adaptive learning...

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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...

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Application Information

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IPC IPC(8): G06F16/9536G06N3/04G06N3/08G06N7/00
CPCG06F16/9536G06N3/08G06N3/047G06N3/048G06N7/01G06N3/045
Inventor 李斌权僧德文
Owner HANGZHOU DIANZI UNIV
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