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：早期基于图神经网络（GNN）的会话推荐模型受限于有限的会话内信息，导致性能瓶颈。因此，部分基于GNN的会话推荐模型逐步引入额外的会话间信息以辅助下一项预测。然而，本研究发现会话间信息的引入可能对这些模型带来干扰，原因有二：其一，会话间依赖关系未在因子层面进行区分；其二，仅通过相似性度量会话间权重存在不足。本文提出DEISI模型解决上述问题。针对第一个问题，DEISI借助深度强化学习（DRL）技术在因子层面区分会话间依赖类型；针对第二个问题，DEISI引入稳定性作为新指标，与相似性共同加权会话间依赖关系。此外，采用对比学习（CL）提升模型鲁棒性。在三个数据集上的大量实验表明，DEISI模型相较于现有最优模型展现出更优越的性能。