ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation
Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, Jie Tang
Abstract
In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.