User Matching Method for Cross Social Networks Based on Deep Learning

MA Qiang; DAI Jun

doi:10.11999/JEIT220702

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2650-2658

MA Qiang, DAI Jun. User Matching Method for Cross Social Networks Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2650-2658. doi: 10.11999/JEIT220702

Citation:

MA Qiang, DAI Jun. User Matching Method for Cross Social Networks Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2650-2658. doi: 10.11999/JEIT220702

MA Qiang, DAI Jun. User Matching Method for Cross Social Networks Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2650-2658. doi: 10.11999/JEIT220702

Citation:

MA Qiang, DAI Jun. User Matching Method for Cross Social Networks Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2650-2658. doi: 10.11999/JEIT220702

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User Matching Method for Cross Social Networks Based on Deep Learning

doi: 10.11999/JEIT220702

MA Qiang,
DAI Jun^,

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

Funds: The National Natural Science Foundation of China (62071170，62072158), Henan Science Foundation for Distinguished Young Scholars (222300420006), Henan Support Plan for Science and Technology Innovation Team of Universities (21IRTSTHN015), The Doctoral Foundation of Southwest University of Science and Technology (17zx7158)

Received Date: 2022-05-31
Accepted Date: 2022-09-06
Rev Recd Date: 2022-08-27

Available Online: 2022-09-09

Publish Date: 2023-07-10

Abstract

Abstract

The existing spatio-temporal information based user matching schemes for cross social networks have problems of spatio-temporal information decoupling and feature extraction difficulties, which result in a decrease in matching accuracy. A Deep Learning based User Matching method for Cross social Networks (DLUMCN) is proposed. Firstly, grid mapping at the spatio-temporal scale is carried out on the user sign-in data. The sign-in matrix set is generated, which contains user characteristics. User sign-in map is formed after normalization. Secondly, the convolution is used to generate high-dimensional spatio-temporal feature maps from the user sign-in map. The weight transformation and feature fusion of feature maps are carried out by deep separable convolution. The feature vector is obtained by one-dimensional expansion of feature maps. Finally, the fully connected feedforward network is used to build a classifier and output the user matching score. Experimental results on two sets of datasets of real social networks show that the proposed method has improved matching accuracy and F1-value, compared with the existing related methods. The effectiveness of the proposed method is demonstrated.
- Cross social networks,
- User matching,
- Deep learning,
- Sign-in similarity

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References(22)

References

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