• Corpus ID: 220250619

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

@inproceedings{Yu2020GraphCN,
  title={Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters},
  author={Wenhui Yu and Zheng Qin},
  booktitle={ICML},
  year={2020}
}
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{LCF}) to… 

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References

SHOWING 1-10 OF 41 REFERENCES
Spectral collaborative filtering
TLDR
Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF.
Graph Convolutional Matrix Completion
TLDR
A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
TLDR
This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
TLDR
This tutorial overview outlines the main challenges of the emerging field of signal processing on graphs, discusses different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlights the importance of incorporating the irregular structures of graph data domains when processing signals on graphs.
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
Spectrum-enhanced Pairwise Learning to Rank
TLDR
Spectral features extracted from two hypergraph structures of the purchase records are introduced and used to model the users' preference and items' properties by incorporating them into a Matrix Factorization (MF) model, which outperform several state-of-the-art models significantly.
Neural Graph Collaborative Filtering
TLDR
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
A novel method based on highly efficient random walks to structure the convolutions and a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model are developed.
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
TLDR
This paper proposes a novel approach to overcome limitations of matrix completion techniques by using geometric deep learning on graphs, and applies this method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.
...
...