通过动态注意力与噪声正则化提升推荐模型鲁棒性。
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
- 引入逐层噪声注入的混合注意力机制
- 在多个数据集上显著优于现有自注意力模型
- 适合需要高泛化能力的实时推荐场景
序列推荐模型通过自注意力机制已达到顶尖性能。研究表明，仅使用物品ID和位置嵌入不足以充分建模用户行为，引入多维核嵌入与时间上下文核可显著提升预测准确率。本文进一步提出自适应鲁棒序列推荐框架（ADRRec），通过引入混合注意力机制与逐层噪声注入（LNI）正则化，增强模型鲁棒性与泛化能力。大量实验表明，该模型在多个基准数据集上均优于现有自注意力架构。
原文摘要 · Abstract (English)
Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. It has since been found that moving beyond only using item ID and positional embeddings leads to a significant accuracy boost when predicting the next item. In recent literature, it was reported that a multi-dimensional kernel embedding with temporal contextual kernels to capture users' diverse behavioral patterns results in a substantial performance improvement. In this study, we further improve the sequential recommender model's robustness and generalization by introducing a mix-attention mechanism with a layer-wise noise injection (LNI) regularization. We refer to our proposed model as adaptive robust sequential recommendation framework (ADRRec), and demonstrate through extensive experiments that our model outperforms existing self-attention architectures.
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