Predicting user actions based on anonymous sessions is a challenge to general recommendation systems because the lack of user profiles heavily limits data-driven models. Recently, session-based recommendation methods have achieved remarkable results in dealing with this task. However, the upper bound of performance can still be boosted through the innovative exploration of limited data. In this paper, we propose a novel method, namely Intra-and Inter-session Interaction-aware Graph-enhanced Network, to take inter-session item-level interactions into account. Different from existing intra-session item-level interactions and session-level collaborative information, our introduced data represents complex item-level interactions between different sessions. For mining the new data without breaking the equilibrium of the model between different interactions, we construct an intra-session graph and an inter-session graph for the current session. The former focuses on item-level interactions within a single session and the latter models those between items among neighborhood sessions. Then different approaches are employed to encode the information of two graphs according to different structures, and the generated latent vectors are combined to balance the model across different scopes. Experiments on real-world datasets verify that our method outperforms other state-of-the-art methods.

本文提出了一种新的方法——Intra-and Inter-session Interaction-aware Graph-enhanced Network，用于考虑不同会话之间的项级交互，通过构建会话内图和会话间图，实现了对新数据的挖掘，并通过编码两个图的信息和生成的潜在向量的结合来平衡模型在不同范围内的性能，进而实现了优于其他最先进方法的实验结果。

基于会话的推荐系统中的多级交互平衡