Keywords: Graph Neural Network, Collaborative Effect, Graph Isomorphism, Recommender Systems
Abstract: Graph Neural Networks (GNNs) have been successfully adopted in recommendation systems by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless, most of the existing message-passing mechanisms for recommendation are directly inherited from GNNs without scrutinizing whether the captured collaborative effect would benefit the prediction of user preferences. In this paper, we first analyze how message-passing captures the collaborative effect in predicting user preferences and propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighbors. By empirically demonstrating the benefits of leveraging collaborations from neighbors with higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph Convolution Network (CAGCN), that goes beyond 1-WL test in distinguishing non-bipartite-subgraph-isomorphic graphs. Experiments on six benchmark datasets show that the best CAGCN variant outperforms the most representative GNN-based recommendation model, LightGCN, by nearly 10% in Recall@20 and also achieves more than 80% speedup.
PDF File: pdf
Type Of Submission: Extended abstract (max 4 main pages).
Agreement: Check this if you are okay with being contacted to participate in an anonymous survey.
Type Of Submission: Extended abstract.