Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences. We revisit SR from a novel information-theoretic perspective and find that conventional sequential modeling methods fail to adequately capture the randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the DDSR model, which uses fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests. Formally based on diffusion transition processes in discrete state spaces, which is unlike common diffusion models such as DDPM that operate in continuous domains. It is better suited for discrete data, using structured transitions instead of arbitrary noise introduction to avoid information loss. Additionally, to address the inefficiency of matrix transformations due to the vast discrete space, we use semantic labels derived from quantization or RQ-VAE to replace item IDs, enhancing efficiency and improving cold start issues. Testing on three public benchmark datasets shows that DDSR outperforms existing state-of-the-art methods in various settings, demonstrating its potential and effectiveness in handling SR tasks.

本研究解决了传统序列推荐方法无法充分捕捉用户行为中的随机性和不可预测性的问题。提出的DDSR模型基于模糊信息处理理论，利用交互序列的模糊集合，通过离散状态空间的扩散转移过程来更好地捕捉用户真实兴趣的演变。实验表明，DDSR在处理序列推荐任务时优于现有的最先进的方法，展现了其潜力和有效性。

打破决定论：使用离散状态空间扩散模型的序列推荐模糊建模