.png){kind=icon}
返回
Cross-social-network user alignment research based on multi-dimensional user features
.png){kind=icon}
OA
AI
DOI：10.1080/21642583.2024.2436660.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate alignment accuracy. To address these challenges, we introduce a cross-social network user alignment model based on multi-dimensional user features (MDUF). First, inspired by the principles of entity recognition and Hartley's association method, we employed a block matrix association algorithm to project the original dataset into different high-dimensional spaces. Second, we proposed a new inertia weight calculation method to improve the convergence speed from linear to nonlinear transformations. This method improves the performance of traditional particle swarm optimization algorithms. Finally, we utilize improved particle swarm optimization and residual connection techniques to optimize bidirectional long short-term memory networks. The experimental results show that our proposed model significantly outperforms traditional alignment models in terms of alignment efficiency and accuracy, which is highly practical and has the potential to inspire further research on social network user alignment.
Keyword:
Social networks
user alignment
block matrix
particle swarm optimization algorithm
期刊
IF:
4.4
论文数:
486
被引数:
2.1K
机构
引用论文
Assessment of diarrhea as side effect of oral targeted antineoplastic agents in clinical practice
PREAI
分享收藏Quantitative assessment of the spatial crowding heterogeneity in cellular fluids细胞流体中空间拥挤异质性的定量评估
PREAI
分享收藏Refractive index sensor of Mach–Zehnder interferometer based on thermo-optic effect of SOI waveguide.png)
Optik
OptikIF0
PREAI
分享收藏
查看更多内容