Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information. First, the self-attention mechanism is added to convolutional matrix fac...

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Bibliographic Details
Main Authors: Chenkun Zhang, Cheng Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Recommendation algorithm
self-attention mechanism
convolutional neural networks
probabilistic matrix factorization
item auxiliary information
Online Access:https://ieeexplore.ieee.org/document/9261574/

Internet

https://ieeexplore.ieee.org/document/9261574/

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