Session-based recommendation which has been witnessed a booming interest recently, focuses on predicting a user's next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to well distinguish causality and correlation relationship. Considering the varied interpretations and characteristics of causality and correlation relationship between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationship between items. In particular, we construct cause, effect and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network-based method for session-based recommendation. To conclude, we strive to explore the relationship between items from specific ``causality" (directed) and ``correlation" (undirected) perspectives. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR, and demonstrate the explainability of our model via case studies on Amazon dataset.

翻译：会话推荐近年来受到广泛关注，其核心任务是基于匿名会话预测用户下一个感兴趣的项目。现有研究大多采用复杂的深度学习技术（如图神经网络）以实现高效的会话推荐。然而，这些方法仅处理项目间的共现关系，未能有效区分因果关系与相关性。考虑到项目间因果与相关关系的不同语义特征及属性，本文提出一种名为CGSR的新方法，通过联合建模项目间的因果关系与相关关系。具体而言，我们基于会话数据构建原因图、结果图及相关图，同时解决虚假因果问题，并进一步设计基于图神经网络的会话推荐方法。综上所述，本文从定向"因果关系"与无向"相关关系"视角探索项目间关系。在三个数据集上的大量实验表明，我们的模型在推荐准确性上优于现有最优方法。此外，我们进一步提出CGSR的可解释框架，并通过Amazon数据集上的案例研究验证了模型的可解释性。