Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation
This work addresses the challenge of making session-based recommendations more effective and explainable for users by focusing on causal relationships, representing an incremental improvement over existing deep learning approaches.
The paper tackles the problem of distinguishing causality from correlation in session-based recommendation by proposing CGSR, a method that jointly models both relationships using graph neural networks, resulting in improved recommendation accuracy over state-of-the-art methods on three datasets.
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.