论文标题
用于协作过滤的强大层次图卷积网络模型
A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering
论文作者
论文摘要
Graph卷积网络（GCN）取得了巨大的成功，并已应用于包括推荐系统在内的各个领域。但是，GCN仍然遇到许多问题，例如训练困难，过度平滑，容易受到对抗性攻击等。与当前基于GCN的方法不同，这些方法仅采用GCN进行推荐，在本文中，我们致力于为协作过滤构建强大的GCN模型。首先，我们认为，从不同顺序的邻里递归合并消息会使不同的节点消息混合在一起，这增加了训练难度。取而代之的是，我们选择使用简单的GCN模型分别汇总不同的邻居消息，该消息已显示为有效。然后，我们以分层的方式将它们累积在一起，而无需引入其他模型参数。其次，我们提出了一种解决方案，以通过在每一层中随机删除邻居消息来减轻过度光滑，这也很好地防止了过度拟合并增强鲁棒性。在三个现实世界数据集上进行了广泛的实验证明了我们模型的有效性和鲁棒性。
Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such as training difficulties, over-smoothing, vulnerable to adversarial attacks, etc. Distinct from current GCN-based methods which simply employ GCN for recommendation, in this paper we are committed to build a robust GCN model for collaborative filtering. Firstly, we argue that recursively incorporating messages from different order neighborhood mixes distinct node messages indistinguishably, which increases the training difficulty; instead we choose to separately aggregate different order neighbor messages with a simple GCN model which has been shown effective; then we accumulate them together in a hierarchical way without introducing additional model parameters. Secondly, we propose a solution to alleviate over-smoothing by randomly dropping out neighbor messages at each layer, which also well prevents over-fitting and enhances the robustness. Extensive experiments on three real-world datasets demonstrate the effectiveness and robustness of our model.
