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Found 4 papers, 2 papers with code
A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering
Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems.
Less is More: Reweighting Important Spectral Graph Features for Recommendation
To unveil the effectiveness of GCNs for recommendation, we first analyze them in a spectral perspective and discover two important findings: (1) only a small portion of spectral graph features that emphasize the neighborhood smoothness and difference contribute to the recommendation accuracy, whereas most graph information can be considered as noise that even reduces the performance, and (2) repetition of the neighborhood aggregation emphasizes smoothed features and filters out noise information in an ineffective way.
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance.
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence.
