  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.
