论文标题
因果关系和相关图建模，用于有效且可解释的建议建议
Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation
论文作者
论文摘要
基于会话的推荐最近见证了蓬勃发展的兴趣，重点是基于匿名会话来预测用户的下一个感兴趣的项目。大多数现有研究采用复杂的深度学习技术（例如图形神经网络），以有效的基于会话的建议。但是，它们只是解决项目之间的共发生，但无法很好地区分因果关系和相关关系。考虑到项目之间因果关系和相关关系的各种解释和特征，在这项研究中，我们提出了一种新的方法，通过共同建模项目之间的因果关系和相关关系表示为CGSR。特别是，我们通过考虑错误的因果关系问题来构建会话中的原因，效果和相关图。我们进一步设计了一种基于图的神经网络方法，用于基于会话的建议。总而言之，我们努力从特定的``因果关系''（指示）和``相关性''（无方向的观点探索项目之间的关系。在三个数据集上进行的广泛实验表明，就建议精度而言，我们的模型优于其他最先进的方法。此外，我们进一步提出了一个关于CGSR的可解释框架，并通过亚马逊数据集上的案例研究证明了我们的模型的解释性。
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.
