Single-Layer Graph Convolutional Networks For Recommendation

• URL: http://arxiv.org/abs/2006.04164v1
• Date: Sun, 7 Jun 2020 14:38:47 GMT
• Title: Single-Layer Graph Convolutional Networks For Recommendation
• Authors: Yue Xu and Hao Chen and Zengde Deng and Junxiong Zhu and Yanghua Li and Peng He and Wenyao Gao and Wenjun Xu
• Abstract summary: Graph Convolutional Networks (GCNs) have received significant attention and achieved start-of-the-art performances on recommendation tasks. Existing GCN models tend to perform recursion aggregations among all related nodes, which arises severe computational burden. We propose a single GCN layer to aggregate information from the neighbors filtered by DA similarity and then generates the node representations.
• Score: 17.3621098912528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Convolutional Networks (GCNs) and their variants have received significant attention and achieved start-of-the-art performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which arises severe computational burden. Moreover, they favor multi-layer architectures in conjunction with complicated modeling techniques. Though effective, the excessive amount of model parameters largely hinder their applications in real-world recommender systems. To this end, in this paper, we propose the single-layer GCN model which is able to achieve superior performance along with remarkably less complexity compared with existing models. Our main contribution is three-fold. First, we propose a principled similarity metric named distribution-aware similarity (DA similarity), which can guide the neighbor sampling process and evaluate the quality of the input graph explicitly. We also prove that DA similarity has a positive correlation with the final performance, through both theoretical analysis and empirical simulations. Second, we propose a simplified GCN architecture which employs a single GCN layer to aggregate information from the neighbors filtered by DA similarity and then generates the node representations. Moreover, the aggregation step is a parameter-free operation, such that it can be done in a pre-processing manner to further reduce red the training and inference costs. Third, we conduct extensive experiments on four datasets. The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.

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