用双注意力卷积专家网络提升稀疏张量补全效果
Dual-Attention Convolution Experts for Sparse Tensor Completion
- 设计多通道卷积网络生成多样非线性对齐模式
- 通过分组对比学习增强自监督信号，缓解极端稀疏问题
- 在交通与推荐场景中优于现有方法，适合高维稀疏数据
张量分解（TF）被广泛用于高维稀疏数据补全任务。尽管进展显著，神经张量分解方法常难以捕捉复杂的跨模态交互，且对（极端）数据稀疏仍敏感。为此，我们提出一种新型神经张量分解方法——带分组对比学习的双注意力卷积专家网络（DCGC）。针对第一类问题，DCGC通过多通道卷积网络生成潜因子的多样化非线性对齐模式，并利用门控双注意力机制引导模型关注更重要的输出通道（即卷积专家）和对齐特征。此外，DCGC引入分组级对比学习策略，聚合相同反馈等级的正样本，分离不同等级间的负样本，从而注入高质量自监督信号以缓解数据稀疏。在五个数据集上的大量实验表明，我们的DCGC在交通与推荐应用的稀疏张量补全任务中优于当前最优方法。论文实验结果代码已公开于 https://github.com/ku1z/DCGC。
原文摘要 · Abstract (English)
Tensor factorization (TF) has been widely adopted for high-dimensional sparse data completion tasks. Despite significant progress, neural TF methods often struggle to capture complex cross-mode interactions and remain vulnerable to (extreme) data sparsity. To address these challenges, we propose a novel neural tensor factorization approach, termed Dual-Attention Convolution Expert Networks with Group-Level Contrastive Learning (DCGC). For the first problem, DCGC generates diverse non-linear alignment patterns of latent factors via a multi-channel convolution network, and leverages the gated dual-attention mechanism to drive the model to focus on more important output channels (i.e., convolution experts) and the aligned features. Furthermore, DCGC introduces a group-level contrastive learning strategy that aggregates positive samples with identical feedback levels while separating negative samples across different levels. This strategy injects high-quality self-supervised signals to mitigate data sparsity. Extensive experiments conducted on five datasets demonstrate that our DCGC outperforms the state-of-the-art methods in sparse tensor completion for traffic and recommendation applications. Code to reproduce the experimental results in the paper is available at https://github.com/ku1z/DCGC.
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