通过去噪与增强构建动态长期推荐嵌入，提升推荐准确性和鲁棒性。
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
- 构建物品图并用社区检测过滤噪声，增强用户-物品交互
- 结合GCN、GRU与注意力机制，动态学习短期与长期偏好
- 在4个真实数据集上优于主流模型，适合长时序推荐场景
互联网的快速发展使个性化推荐系统不可或缺。基于图神经网络（GNN）的序列推荐系统虽能有效捕捉复杂用户-物品交互，但常受噪声干扰且表示静态。本文提出自适应长期嵌入去噪增强推荐方法（ALDA4Rec），构建物品-物品图，通过社区检测过滤噪声，并丰富用户-物品交互。利用图卷积网络（GCNs）学习短期表示，结合平均池化、门控循环单元（GRUs）与注意力机制建模长期嵌入。采用基于MLP的自适应加权策略，动态优化长期用户偏好。在四个真实世界数据集上的实验表明，ALDA4Rec显著优于当前最优基线，在准确率与鲁棒性方面均有提升。源代码见https://github.com/zahraakhlaghi/ALDA4Rec。
原文摘要 · Abstract (English)
The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, powered by Graph Neural Networks (GNNs), effectively capture complex user-item interactions but often face challenges such as noise and static representations. In this paper, we introduce the Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation (ALDA4Rec) method, a novel model that constructs an item-item graph, filters noise through community detection, and enriches user-item interactions. Graph Convolutional Networks (GCNs) are then employed to learn short-term representations, while averaging, GRUs, and attention mechanisms are utilized to model long-term embeddings. An MLP-based adaptive weighting strategy is further incorporated to dynamically optimize long-term user preferences. Experiments conducted on four real-world datasets demonstrate that ALDA4Rec outperforms state-of-the-art baselines, delivering notable improvements in both accuracy and robustness. The source code is available at https://github.com/zahraakhlaghi/ALDA4Rec.
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