针对现有序列推荐模型在数据稀疏场景下性能不佳，且数据增强方法多为随机扰动、易忽略项目属性信息而导致语义偏离的问题，提出一种侧信息驱动的序列推荐增强模型ADAS-Rec。该模型旨在提升推荐的准确性与鲁棒性。其核心机制在于利用项目多维属性信息显式指导数据增强过程，具体设计了四种协同操作：属性相似替换、属性相似插入、属性引导的剪裁与掩码。这些操作旨在生成多样化且与用户细粒度偏好在属性层面语义一致的高质量增强序列，有效缓解数据稀疏问题。在此基础上，为提纯序列信号，集成了一个频域滤波模块，通过抑制序列噪声并强化关键信号特征，为后续偏好学习提供更优的输入。最终，采用一种包含引导聚焦层和因果自注意力模块的双重注意力机制，以精确捕捉经过增强和滤波的序列中所蕴含的用户动态偏好以及深层时序依赖，从而生成精准的推荐。在Beauty、Sports和Yelp三个公开数据集上的实验结果表明，所提出的ADAS-Rec模型在常用的Recall@10和NDCG@10等评价指标上均显著优于多种先进的基线方法。

To address the poor performance of existing sequential recommendation models in data-sparse scenarios and the semantic deviation problem caused by random-perturbation-based data augmentation methods that often ignore item attributes, a side information-driven sequential recommendation enhancement model (ADAS-Rec) is proposed. The model aims to improve the accuracy and robustness of recommendations. Its core mechanism lies in explicitly guiding the data augmentation process using multi-dimensional item attribute information. Specifically, four synergistic operations are designed: attribute similarity substitution, attribute similarity insertion, and attribute-guided cropping and masking. These operations are intended to generate diverse, high-quality augmented sequences that are semantically consistent with users' fine-grained preferences at the attribute level, effectively alleviating the data sparsity problem. On this basis, to purify the sequence signal, a frequency-domain filtering module is integrated to provide a better input for subsequent preference learning by suppressing sequence noise and enhancing key signal features. Finally, a dual attention mechanism, comprising a guided focus layer and a causal self-attention module, is employed to accurately capture the dynamic user preferences and deep temporal dependencies embedded in the enhanced and filtered sequences, thereby generating precise recommendations. Experimental results on three public datasets, Beauty, Sports, and Yelp, demonstrate that the proposed ADAS-Rec model significantly outperforms various state-of-the-art baseline methods on common evaluation metrics such as Recall@10 and NDCG@10.