通过强化注意多关系图神经网络实现的预训练推荐系统

近年来，图神经网络(GNNs)已经证明了其在推荐系统中的有效性。现有的研究已经应用gnn来捕获数据中的协作关系。然而，在现实场景中，推荐图中的关系可以是各种的。例如，两部电影可以由同一类型或由同一导演/演员相关联。如果我们使用一个单一的图来说明所有这些关系，这个图可能太复杂而无法处理。为了解决这个问题，我们引入了预训练的想法来逐步处理复杂的图。基于分治之法的思想，我们将大图分为三个子图：用户图、项目图和用户项交互图。然后分别从用户图和项目图中对用户嵌入和项目嵌入进行预训练。为了进行预训练，我们分别根据多关系用户图和项目图的属性构建它们。在本文中，我们提出了一种新的增强注意多关系图神经网络(RAM-GNN)对训练前用户和项目图上的项目嵌入。具体来说，我们设计了一个关系级的注意层来学习不同关系的重要性。然后，利用增强邻域采样器(RNS)搜索了对图中顶部k个相似邻居进行采样的最优滤波阈值，避免了过平滑的问题。我们使用预先训练过的用户/项目嵌入来初始化推荐模型。最后，利用基于聚合的GNN模型从用户-项目交互图中的协作关系中学习，并提供建议。我们的实验表明，RAM-GNN优于其他最先进的基于图的推荐模型和多关系图神经网络。

原文题目：Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network

原文：Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes. In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.