Abstract：
Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanations with users and items, graph neural networks (GNN) are usually employed to learn node representations on the heterogeneous user-item-explanation interaction graph. However, modeling heterogeneous graph convolution poses limitations in both message passing styles and computational efficiency, resulting in sub-optimal recommendation performance. To address the limitations, we propose an Explanation-aware Graph Convolution Network (ExpGCN). In particular, the heterogeneous interaction graph is divided to subgraphs regard to the edge types in ExpGCN. By aggregating information from distinct subgraphs, ExpGCN is capable of generating node representations for explanation ranking task and item recommendation task respectively. Task-oriented graph convolution can not only reduce the complexity of heterogeneous node aggregation, but also alleviate the performance degeneration caused by the conflicts between task learning objectives, which has been neglected in current studies. Extensive experiments on four public datasets show that ExpGCN significantly outperforms state-of-the-art baselines with high efficiency, demonstrating the effectiveness of ExpGCN in explainable recommendations.
摘要：
推荐系统中的现有作品已经广泛探索提取评论作为用户项目交互之外的解释，并将解释生成制定为排名任务，以提高项目推荐性能。要将说明与用户和项目关联，图神经网络（GNN）通常用于学习异构用户-项目-解释交互图上的节点表示。然而,建模异构图卷积在消息传递风格和计算效率方面都存在限制，导致次优的推荐性能。为了解决这些限制，我们提出了一种解释感知图卷积网络(ExpGCN)。特别是,关于ExpGCN中的边类型，异构交互图被划分为子图。通过聚集来自不同子图的信息，ExpGCN能够分别为说明排序任务和项目推荐任务生成节点表示。面向任务的图卷积不仅可以降低异构节点聚合的复杂度,同时也缓解了任务学习目标之间的冲突导致的绩效退化，这在当前的研究中被忽略。在四个公共数据集上的大量实验表明，ExpGCN以高效率显著优于最先进的基线，证明ExpGCN在可解释建议中的有效性。
