  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.
