用注意力机制挖掘跨平台打卡数据中的时空共现模式，提升用户身份关联准确率。
Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data
- 基于相关性注意力的掩码时序变换器，聚焦跨平台打卡行为的时空共现点。
- 在多个数据集上相比最优基线，宏F1提升12.92%~17.76%，AUC提升5.80%~8.38%。
- 适合处理噪声多、稀疏的跨平台用户行为数据，适用于社交网络身份对齐场景。
随着社交媒体和基于位置的社交网络（LBSN）的发展，跨平台打卡数据已成为用户身份关联（UIL）的关键。这些数据不仅反映用户的时空信息，还揭示其行为模式与兴趣偏好。然而，跨平台身份关联面临数据质量差、高稀疏性和噪声干扰等挑战，制约了现有方法对跨平台用户信息的提取。为此，本文提出一种基于变换器的关联网络——相关性注意力掩码时序变换器（MT-Link），通过学习跨平台用户打卡序列的时空共现模式来提升模型性能。该模型采用相关性注意力机制，有效识别用户打卡序列间的时空共现关系，利用注意力权重图聚焦共现点并过滤噪声，从而提升分类效果。实验表明，该模型在宏F1上优于当前最优基线12.92%~17.76%，在AUC上提升5.80%~8.38%。
原文摘要 · Abstract (English)
With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data quality, high sparsity, and noise interference, which hinder existing methods from extracting cross-platform user information. To address these issues, we propose a Correlation-Attention Masked Transformer for User Identity Linkage Network (MT-Link), a transformer-based framework to enhance model performance by learning spatio-temporal co-occurrence patterns of cross-platform users. Our model effectively captures spatio-temporal co-occurrence in cross-platform user check-in sequences. It employs a correlation attention mechanism to detect the spatio-temporal co-occurrence between user check-in sequences. Guided by attention weight maps, the model focuses on co-occurrence points while filtering out noise, ultimately improving classification performance. Experimental results show that our model significantly outperforms state-of-the-art baselines by 12.92%~17.76% and 5.80%~8.38% improvements in terms of Macro-F1 and Area Under Curve (AUC).
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