Session-based recommendation aims to predict the next item that anonymous users may be interested in, based on their current session interactions. Recent studies have demonstrated that retrieving neighbor sessions to augment the current session can effectively alleviate the data sparsity issue and improve recommendation performance. However, existing methods typically rely on explicitly observed session data, neglecting latent neighbors - not directly observed but potentially relevant within the interest space - thereby failing to fully exploit the potential of neighbor sessions in recommendation. To address the above limitation, we propose a novel model of diffusion-based latent neighbor generation for session-based recommendation, named DiffSBR. Specifically, DiffSBR leverages two diffusion modules, including retrieval-augmented diffusion and self-augmented diffusion, to generate high-quality latent neighbors. In the retrieval-augmented diffusion module, we leverage retrieved neighbors as guiding signals to constrain and reconstruct the distribution of latent neighbors. Meanwhile, we adopt a training strategy that enables the retriever to learn from the feedback provided by the generator. In the self-augmented diffusion module, we explicitly guide the generation of latent neighbors by injecting the current session's multi-modal signals through contrastive learning. After obtaining the generated latent neighbors, we utilize them to enhance session representations for improving session-based recommendation. Extensive experiments on four public datasets show that DiffSBR generates effective latent neighbors and improves recommendation performance against state-of-the-art baselines.

翻译：会话推荐旨在根据匿名用户的当前会话交互预测其可能感兴趣的下一个项目。近期研究表明，通过检索邻居会话来增强当前会话能够有效缓解数据稀疏性问题并提升推荐性能。然而，现有方法通常依赖于显式观测到的会话数据，忽略了潜在邻居——即未直接观测但在兴趣空间中可能相关的会话——从而未能充分挖掘邻居会话在推荐中的潜力。为突破这一局限，我们提出一种基于扩散的潜在邻居生成新模型DiffSBR，用于会话推荐。具体而言，DiffSBR利用两个扩散模块——检索增强扩散与自增强扩散——来生成高质量的潜在邻居。在检索增强扩散模块中，我们以检索到的邻居作为引导信号，约束并重构潜在邻居的分布。同时，我们采用一种训练策略使检索器能够从生成器提供的反馈中学习。在自增强扩散模块中，我们通过对比学习注入当前会话的多模态信号，显式引导潜在邻居的生成。获得生成的潜在邻居后，我们利用它们增强会话表征以改进会话推荐。在四个公开数据集上的大量实验表明，DiffSBR能够生成有效的潜在邻居，并在推荐性能上优于现有先进基线方法。