Limited intra-session information is the performance bottleneck of the early GNN based SBR models. Therefore, some GNN based SBR models have evolved to introduce additional inter-session information to facilitate the next-item prediction. However, we found that the introduction of inter-session information may bring interference to these models. The possible reasons are twofold. First, inter-session dependencies are not differentiated at the factor-level. Second, measuring inter-session weight by similarity is not enough. In this paper, we propose DEISI to solve the problems. For the first problem, DEISI differentiates the types of inter-session dependencies at the factor-level with the help of DRL technology. For the second problem, DEISI introduces stability as a new metric for weighting inter-session dependencies together with the similarity. Moreover, CL is used to improve the robustness of the model. Extensive experiments on three datasets show the superior performance of the DEISI model compared with the state-of-the-art models.

翻译：限制性的会话内信息是早期基于GNN的SBR模型的性能瓶颈。因此，一些基于GNN的SBR模型发展出引入额外的会话间信息来促进下一项预测。然而，我们发现会话间信息的引入可能会给这些模型带来干扰。可能的原因有两个。首先，会话间依赖关系在因素级别上没有区分。其次，通过相似度来衡量会话间权重是不够的。在本文中，我们提出DEISI来解决这些问题。对于第一个问题，DEISI借助DRL技术在因素级别上区分会话间依赖类型。对于第二个问题，DEISI引入稳定性作为权衡会话间依赖关系的新度量标准，与相似度一起使用。此外，还使用了CL 来提高模型的鲁棒性。对三个数据集进行了广泛实验，结果表明DEISI模型优于现有的最先进模型。