  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.
