1 Title 

        DiffuRec: A Diffusion Model for Sequential Recommendation（ZIHAO LI、CHENLIANG L、AIXIN SUN）【2023 ACM Transactions on Information Systems】

2 Conclusion

        This paper is the first attempt to apply the diffusion model to SR, and proposes DiffuRec for the construction of item representations and the injection of uncertainty.

3 Sentences

        1、All these mainstream methods learn item representation as an embedding vector. However, we believe a fixed vector may have limited capability in capturing the following four characteristics simultaneously.（The shortcomings of previous works of RS）
        2、Diffusion models have made remarkable success in CV, NLP and many other fields.With the merits of its distribution generation and diversity representation, we consider Diffusion model to be a good fit to sequential recommendation. In this paper, we thereby make the very first attempt to bridge the gap between diffusion model and sequential recommendation, and propose DiffuRe
        3、

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针对传统推荐算法存在的表征能力有限、不确定性等挑战，本文提出一种利用扩散模型进行序列推荐的工作，该工作能够实现高质量、多样性的推荐效果。

研究动机：现有的SR在商品多维潜在表征建模、用户多兴趣表征建模、推荐的不确定性、推荐的不确定性等方面存在缺陷

DiffuRec模型：

DiffuRec模型结构如图所示，主要包括三个部分：1）逼近器（Approximator）；2）前向扩散过程（Diffusion Phase）；3）后向逆扩散过程（Reversion Phase）。

开源代码：GitHub - WHUIR/DiffuRec