解决推荐序列中缺失数据导致的引导信号失效问题。
Enhancing guidance for missing data in diffusion-based sequential recommendation
- 通过双重采样识别用户兴趣突变点，定位关键行为
- 构建反事实注意力机制，动态重加权重要项目
- 无需额外计算开销，适合真实场景落地使用
当前序列推荐方法正从分类转向基于扩散的生成范式，但观测序列中的缺失数据会削弱用户信息的引导质量，导致生成效果不佳。现有方法仅移除局部相似项，却忽略了用户兴趣的关键转折点，这些转折点对预测后续意图至关重要。为此，我们提出一种新型反事实注意力调节扩散模型（CARD），聚焦于增强关键兴趣转折点项目的信号，同时识别并抑制序列中的噪声。CARD包含：(1) 双侧汤普森采样法识别发生显著兴趣转变的序列；(2) 反事实注意力机制量化各项目的重要性。由此，为扩散模型提供由动态重加权交互向量构成的高质量引导信号，实现有效生成。实验表明，该方法在真实数据上表现优异且计算开销低。代码已公开于 https://github.com/yanqilong3321/CARD。
原文摘要 · Abstract (English)
Contemporary sequential recommendation methods are becoming more complex, shifting from classification to a diffusion-guided generative paradigm. However, the quality of guidance in the form of user information is often compromised by missing data in the observed sequences, leading to suboptimal generation quality. Existing methods address this by removing locally similar items, but overlook ``critical turning points'' in user interest, which are crucial for accurately predicting subsequent user intent. To address this, we propose a novel Counterfactual Attention Regulation Diffusion model (CARD), which focuses on amplifying the signal from key interest-turning-point items while concurrently identifying and suppressing noise within the user sequence. CARD consists of (1) a Dual-side Thompson Sampling method to identify sequences undergoing significant interest shift, and (2) a counterfactual attention mechanism for these sequences to quantify the importance of each item. In this manner, CARD provides the diffusion model with a high-quality guidance signal composed of dynamically re-weighted interaction vectors to enable effective generation. Experiments show our method works well on real-world data without being computationally expensive. Our code is available at https://github.com/yanqilong3321/CARD.
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