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.

翻译：序列推荐（SR）旨在根据用户的历史行为序列预测其可能感兴趣的物品。本文从一个新颖的信息论视角重新审视序列推荐，发现传统的序列建模方法未能充分捕捉用户行为的随机性与不可预测性。受模糊信息处理理论的启发，本文提出了DDSR模型，该模型利用交互序列的模糊集合来克服现有局限，更好地捕捉用户真实兴趣的演化过程。该模型形式化地基于离散状态空间中的扩散转移过程，这与DDPM等在连续域操作的常见扩散模型不同。它更适用于离散数据，通过结构化转移而非任意噪声引入来避免信息损失。此外，为解决因离散空间巨大而导致的矩阵变换效率低下问题，我们使用量化或RQ-VAE生成的语义标签替代物品ID，从而提升效率并改善冷启动问题。在三个公开基准数据集上的测试表明，DDSR在各种设定下均优于现有最先进方法，证明了其在处理序列推荐任务中的潜力与有效性。