StableGCN: Decoupling and Reconciling Information Propagation for Collaborative Filtering

Cong Xu, Jun Wang, Wei Zhang

Abstract: Graph Convolutional Networks (GCNs) have been widely applied to collaborative filtering, where each layer typically contains neighborhood aggregation and feature transformation. Recent studies have found that feature transformation contributes little to the final recommendation performance. They however eliminated it directly without further exploration, leading to a degradation of model expressive power. In this paper, we show that this problem arises from inconsistent information propagation process, in which the dominance of feature transformation prevents features from being properly smoothed by neighborhood aggregation. To this end, we present StableGCN to decouple and reconcile this contradictory process in an orderly rather than intertwined manner. The coarse-grained node features are first refined by an elaborate extractor, and then smoothed by a specific kind of GCN concerning feature denoising. Consequently, feature transformation and neighborhood aggregation can support each other without sacrificing expressive power. Extensive experiments on six public datasets demonstrate the effectiveness and state-of-the-art performance of StableGCN.