STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

Abstract

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Inductive Representation Learning on Large Graphs
• Geometric deep learning: going beyond Euclidean data
• Graph Convolutional Matrix Completion
• Variational Graph Auto-Encoders
• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
• A Neural Autoregressive Approach to Collaborative Filtering
• Neural Network Matrix Factorization
• Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
• Deep Models of Interactions Across Sets
• Difficulty Controllable Generation of Reading Comprehension Questions
• Learning to Rank Using Localized Geometric Mean Metrics

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• Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
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• Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
• A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
• Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks