用语义相似性引导扩散，提升序列推荐的上下文保持能力
SimDiffRec: Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation
- 基于物品嵌入相似性生成语义一致的噪声，避免随机扰动破坏上下文
- 通过高置信度得分选择去噪位置，增强正负样本区分度
- 在5个数据集上超越现有模型，适合追求推荐精度的开发者
在序列推荐系统中，扩散模型被用于数据增强与对比学习以实现鲁棒表征学习。然而，现有方法多采用随机增强，可能破坏原始序列的上下文信息。为此，我们提出SimDiffRec：一种语义相似性引导的对比序列推荐扩散框架。该框架利用物品嵌入向量间的相似性生成语义一致的噪声，并在去噪过程中使用高置信度得分选择增强位置，从而更有效地保留上下文与结构信息。从对比学习角度看，所提增强策略结合硬负样本采样，生成更具区分性的正负样本，同时提升训练效率与推荐性能。在五个基准数据集上的实验结果表明，SimDiffRec显著优于现有基线模型。代码已开源：https://github.com/zingyon/SimDiffRec。
原文摘要 · Abstract (English)
In sequential recommendation systems, data augmentation and contrastive learning techniques have recently been introduced using diffusion models to achieve robust representation learning. However, most of the existing approaches use random augmentation, which risks damaging the contextual information of the original sequence. Accordingly, we propose SimDiffRec: a Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation. Our framework leverages the similarity between item embedding vectors to generate semantically consistent noise. Moreover, we utilize high confidence scores in the denoising process to select our augmentation positions. This approach more effectively reflects contextual and structural information compared to augmentation at random positions. From a contrastive learning perspective, the proposed augmentation technique, combined with hard negative sampling, provides more discriminative positive and negative samples, simultaneously improving training efficiency and recommendation performance. Experimental results on five benchmark datasets show that SimDiffRec outperforms the existing baseline models. The code of our framework is available at https://github.com/zingyon/SimDiffRec.
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