In the era of information explosion, Recommender Systems (RS) are essential for alleviating information overload and providing personalized user experiences. Recent advances in diffusion-based generative recommenders have shown promise in capturing the dynamic nature of user preferences. These approaches explore a broader range of user interests by progressively perturbing the distribution of user-item interactions and recovering potential preferences from noise, enabling nuanced behavioral understanding. However, existing diffusion-based approaches predominantly operate in continuous space through encoded graph-based historical interactions, which may compromise potential information loss and suffer from computational inefficiency. As such, we propose CDRec, a novel Continuous-time Discrete-space Diffusion Recommendation framework, which models user behavior patterns through discrete diffusion on historical interactions over continuous time. The discrete diffusion algorithm operates via discrete element operations (e.g., masking) while incorporating domain knowledge through transition matrices, producing more meaningful diffusion trajectories. Furthermore, the continuous-time formulation enables flexible adaptive sampling. To better adapt discrete diffusion models to recommendations, CDRec introduces: (1) a novel popularity-aware noise schedule that generates semantically meaningful diffusion trajectories, and (2) an efficient training framework combining consistency parameterization for fast sampling and a contrastive learning objective guided by multi-hop collaborative signals for personalized recommendation. Extensive experiments on real-world datasets demonstrate CDRec's superior performance in both recommendation accuracy and computational efficiency.

翻译：在信息爆炸时代，推荐系统对于缓解信息过载和提供个性化用户体验至关重要。基于扩散的生成式推荐模型近期取得进展，展现出捕捉用户偏好动态特性的潜力。这些方法通过逐步扰动用户-物品交互的分布并从噪声中恢复潜在偏好，探索更广泛的用户兴趣范围，实现细粒度的行为理解。然而，现有基于扩散的方法主要通过基于图编码的历史交互在连续空间中操作，可能导致潜在信息损失并存在计算效率问题。为此，我们提出CDRec——一种新颖的连续时间离散空间扩散推荐框架，通过在连续时间上对历史交互进行离散扩散来建模用户行为模式。该离散扩散算法通过离散元素操作（如掩码）运行，同时通过转移矩阵融入领域知识，生成更具语义意义的扩散轨迹。此外，连续时间建模实现了灵活的自适应采样。为使离散扩散模型更好地适应推荐任务，CDRec引入：（1）新颖的流行度感知噪声调度机制，生成语义明确的扩散轨迹；（2）高效训练框架，结合快速采样的一致性参数化与基于多跳协同信号引导的对比学习目标，实现个性化推荐。在真实数据集上的大量实验表明，CDRec在推荐准确性和计算效率方面均具有优越性能。