用于推荐的扩散语言模型
Diffusion Language Model for Recommendation
AI总结：
研究针对现有推荐系统中自回归范式的不足，受扩散语言模型启发，提出 DLMRec。该模型引入协作感知随机分词器、课程驱动训练策略、稳定性感知投票机制，为推荐提供了新的离散扩散语言模型及有效方法。
AI中文摘要：
由大语言模型驱动的推荐系统已成为生成式推荐的一种有前景的范式，利用其强大的语义推理和生成能力来建模复杂多样的用户偏好。然而，现有大多数方法依赖自回归范式，对推荐而言并非最优。下一个 token 目标强调顺序而非用户偏好背后的结构项间依赖。此外，前缀约束生成限制双向上下文并采用从左到右解码，导致早期错误累积且无法纠正。受扩散语言模型成功的启发，我们提出了 DLMRec，一种专为推荐量身定制的离散扩散语言模型，它为自回归生成提供了有吸引力的替代方案。具体而言，DLMRec 引入了三个关键组件来将扩散语言建模与推荐联系起来。首先，一个协作感知随机分词器将多跳协作信号编码为与扩散建模兼容的富有表现力的离散 token。其次，一个课程驱动的训练策略通过渐进式的项级和 token 级学习使去噪过程与偏好恢复对齐。第三，一个稳定性感知投票机制聚合迭代预测以提高生成的一致性和鲁棒性。
英文摘要：
Large language model (LLM)-empowered recommender systems have emerged as a promising paradigm for generative recommendation, leveraging their strong semantic reasoning and generative capacity to model complex, diverse user preferences. However, most existing approaches rely on an autoregressive paradigm that is suboptimal for recommendation. The next-token objective emphasizes sequential order rather than the structural inter-item dependencies underlying user preferences. In addition, prefix-constrained generation restricts bidirectional context and commits to left-to-right decoding, causing early errors to accumulate without correction. Inspired by the success of diffusion language models, we propose \textbf{DLMRec}, a discrete diffusion language model tailored for recommendation that offers a compelling alternative to autoregressive generation. Specifically, DLMRec introduces three key components to bridge diffusion language modeling with recommendation. First, a collaborative-aware stochastic tokenizer encodes multi-hop collaborative signals into expressive discrete tokens compatible with diffusion modeling. Second, a curriculum-driven training strategy aligns the denoising process with preference recovery through progressive item- and token-level learning. Third, a stability-aware voting mechanism aggregates iterative predictions to improve generation consistency and robustness.
