标题： 基于扩散模型的分布外推荐因果负采样 显示英文标题

标题： Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation

摘要： 启发式负采样通过从预定义的候选池中选择不同难度级别的负样本，以引导模型学习更准确的决策边界，从而提高推荐性能。 然而，我们的实证和理论分析表明，候选池中的未观察到的环境混杂因素（例如，曝光或流行度偏差）可能导致启发式采样方法引入虚假困难负样本（FHNS）。 这些误导性样本可能促使模型学习由这些混杂因素引起的虚假相关性，最终在分布变化下损害其泛化能力。 为了解决这个问题，我们提出了一种名为通过扩散进行因果负采样（CNSDiff）的新方法。 通过在潜在空间中通过条件扩散过程合成负样本，CNSDiff避免了预定义候选池带来的偏差，从而降低了生成FHNS的可能性。 此外，它还引入了一个因果正则化项，在负采样过程中显式缓解环境混杂因素的影响，从而产生促进分布外（OOD）泛化的鲁棒负样本。 在四种代表性分布变化场景下的全面实验表明，与最先进的基线方法相比，CNSDiff在所有评估指标上平均提升了13.96%，验证了其在分布外推荐任务中的有效性和鲁棒性。 显示英文摘要

摘要： Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward learning more accurate decision boundaries. However, our empirical and theoretical analyses reveal that unobserved environmental confounders (e.g., exposure or popularity biases) in candidate pools may cause heuristic sampling methods to introduce false hard negatives (FHNS). These misleading samples can encourage the model to learn spurious correlations induced by such confounders, ultimately compromising its generalization ability under distribution shifts. To address this issue, we propose a novel method named Causal Negative Sampling via Diffusion (CNSDiff). By synthesizing negative samples in the latent space via a conditional diffusion process, CNSDiff avoids the bias introduced by predefined candidate pools and thus reduces the likelihood of generating FHNS. Moreover, it incorporates a causal regularization term to explicitly mitigate the influence of environmental confounders during the negative sampling process, leading to robust negatives that promote out-of-distribution (OOD) generalization. Comprehensive experiments under four representative distribution shift scenarios demonstrate that CNSDiff achieves an average improvement of 13.96% across all evaluation metrics compared to state-of-the-art baselines, verifying its effectiveness and robustness in OOD recommendation tasks.