标题： SoREX：面向相关自我路径提取的自解释社会推荐 显示英文标题

标题： SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction

摘要： 社会推荐已被证明在解决用户-项目交互建模中的数据稀疏性问题方面是有效的，这是通过利用社交网络实现的。最近图神经网络（GNNs）的整合进一步提高了当代社会推荐算法的预测准确性。然而，许多基于GNN的社会推荐方法缺乏为其预测提供有意义解释的能力。在本研究中，我们通过引入SoREX，一个自我解释的基于GNN的社会推荐框架来应对这一挑战。SoREX采用了一个增强的朋友推荐的双塔框架，独立地对社会关系和用户-项目交互进行建模，同时联合优化一个辅助任务以强化社会信号。为了提供解释，我们提出了一种新颖的自我路径提取方法。该方法涉及将目标用户的自我网络转换为一系列多跳自我路径，从中提取特定因素和候选感知的自我路径子集作为解释。这个过程通过复杂的子结构分析促进了不同候选项目之间详细比较解释的总结。此外，我们进行了解释重新聚合，以显式地将解释与下游预测相关联，使我们的框架具有内在的自我解释性。在四个广泛采用的基准数据集上进行的全面实验验证了SoREX在预测准确性方面的有效性。此外，定性和定量分析证实了SoREX中提取的解释的有效性。我们的代码和数据可在https://github.com/antman9914/SoREX获得。 显示英文摘要

摘要： Social recommendation has been proven effective in addressing data sparsity in user-item interaction modeling by leveraging social networks. The recent integration of Graph Neural Networks (GNNs) has further enhanced prediction accuracy in contemporary social recommendation algorithms. However, many GNN-based approaches in social recommendation lack the ability to furnish meaningful explanations for their predictions. In this study, we confront this challenge by introducing SoREX, a self-explanatory GNN-based social recommendation framework. SoREX adopts a two-tower framework enhanced by friend recommendation, independently modeling social relations and user-item interactions, while jointly optimizing an auxiliary task to reinforce social signals. To offer explanations, we propose a novel ego-path extraction approach. This method involves transforming the ego-net of a target user into a collection of multi-hop ego-paths, from which we extract factor-specific and candidate-aware ego-path subsets as explanations. This process facilitates the summarization of detailed comparative explanations among different candidate items through intricate substructure analysis. Furthermore, we conduct explanation re-aggregation to explicitly correlate explanations with downstream predictions, imbuing our framework with inherent self-explainability. Comprehensive experiments conducted on four widely adopted benchmark datasets validate the effectiveness of SoREX in predictive accuracy. Additionally, qualitative and quantitative analyses confirm the efficacy of the extracted explanations in SoREX. Our code and data are available at https://github.com/antman9914/SoREX.