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.

翻译：互联网的快速发展使得个性化推荐系统变得不可或缺。基于图神经网络（GNN）的图结构序列推荐系统能够有效捕捉用户与物品之间的复杂交互，但常面临噪声干扰与静态表示等挑战。本文提出面向推荐的自适应去噪与增强长时嵌入方法（ALDA4Rec），该新型模型通过构建物品-物品图、利用社区检测过滤噪声并增强用户-物品交互。模型采用图卷积网络（GCN）学习短期表示，同时运用均值池化、门控循环单元（GRU）及注意力机制建模长时嵌入，并进一步引入基于多层感知器（MLP）的自适应加权策略以动态优化用户长时偏好。在四个真实数据集上的实验表明，ALDA4Rec在准确性与鲁棒性方面均优于当前最优基准模型，取得了显著提升。源代码已开源至https://github.com/zahraakhlaghi/ALDA4Rec。