Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation

TL;DR

This paper introduces CGSR, a novel graph neural network model for session-based recommendation that explicitly models causality and correlation between items to improve accuracy and explainability.

Contribution

It proposes a joint causality and correlation graph modeling approach for session-based recommendation, addressing limitations of prior co-occurrence-based methods.

Findings

Outperforms state-of-the-art methods in recommendation accuracy

Effectively distinguishes causality from correlation in item relationships

Provides an explainable recommendation framework with case studies

Abstract

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.